TorchIO is a Medical image preprocessing and augmentation toolkit for deep learning. Part of the PyTorch Ecosystem.

Overview

TorchIO logo

Tools like TorchIO are a symptom of the maturation of medical AI research using deep learning techniques.

Jack Clark, Policy Director at OpenAI (link).


Package PyPI downloads PyPI version Conda version Contributors
CI Build status Documentation status Coverage status
Code Code quality Code maintainability pre-commit
Tutorials Google Colab
Social Slack Slack

Progressive artifacts

Augmentation


Original Random blur
Original Random blur
Random flip Random noise
Random flip Random noise
Random affine transformation Random elastic transformation
Random affine transformation Random elastic transformation
Random bias field artifact Random motion artifact
Random bias field artifact Random motion artifact
Random spike artifact Random ghosting artifact
Random spike artifact Random ghosting artifact

Queue

(Queue for patch-based training)


TorchIO is a Python package containing a set of tools to efficiently read, preprocess, sample, augment, and write 3D medical images in deep learning applications written in PyTorch, including intensity and spatial transforms for data augmentation and preprocessing. Transforms include typical computer vision operations such as random affine transformations and also domain-specific ones such as simulation of intensity artifacts due to MRI magnetic field inhomogeneity or k-space motion artifacts.

This package has been greatly inspired by NiftyNet, which is not actively maintained anymore.

Credits

If you like this repository, please click on Star!

If you use this package for your research, please cite the paper:

F. Pérez-García, R. Sparks, and S. Ourselin. TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning. Computer Methods and Programs in Biomedicine (June 2021), p. 106236. ISSN: 0169-2607.doi:10.1016/j.cmpb.2021.106236.

BibTeX entry:

@article{perez-garcia_torchio_2021,
    title = {TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning},
    journal = {Computer Methods and Programs in Biomedicine},
    pages = {106236},
    year = {2021},
    issn = {0169-2607},
    doi = {https://doi.org/10.1016/j.cmpb.2021.106236},
    url = {https://www.sciencedirect.com/science/article/pii/S0169260721003102},
    author = {P{\'e}rez-Garc{\'i}a, Fernando and Sparks, Rachel and Ourselin, S{\'e}bastien},
    keywords = {Medical image computing, Deep learning, Data augmentation, Preprocessing},
}

This project is supported by the following institutions:

Getting started

See Getting started for installation instructions and a Hello, World! example.

Longer usage examples can be found in the tutorials.

All the documentation is hosted on Read the Docs.

Please open a new issue if you think something is missing.

Contributors

Thanks goes to all these people (emoji key):


Fernando Pérez-García

💻 📖

valabregue

🤔 👀 💻

GFabien

💻 👀 🤔

G.Reguig

💻

Niels Schurink

💻

Ibrahim Hadzic

🐛

ReubenDo

🤔

Julian Klug

🤔

David Völgyes

🤔 💻

Jean-Christophe Fillion-Robin

📖

Suraj Pai

🤔

Ben Darwin

🤔

Oeslle Lucena

🐛

Soumick Chatterjee

💻

neuronflow

📖

Jan Witowski

📖

Derk Mus

📖 💻

Christian Herz

🐛

Cory Efird

💻

Esteban Vaca C.

🐛

Ray Phan

🐛

Akis Linardos

🐛 💻

Nina Montana-Brown

📖

fabien-brulport

🐛

malteekj

🐛

Andres Diaz-Pinto

🐛

Sarthak Pati

📦

GabriellaKamlish

🐛

Tyler Spears

🐛

DaGuT

📖

Xiangyu Zhao

🐛

siahuat0727

📖 🐛

Svdvoort

💻

Albans98

💻

Matthew T. Warkentin

💻

glupol

🐛

ramonemiliani93

📖

Justus Schock

💻

Stefan Milorad Radonjić

🐛

Sajan Gohil

🐛

This project follows the all-contributors specification. Contributions of any kind welcome!

Comments
  • Reproduce a given transform

    Reproduce a given transform

    🚀 Feature Given the history information saved in the sample for a given transform, how can I apply the exact same transform on a new volume

    Motivation After doing a lot of data augmentation for training and validation, I would like to visually check some specific volume (the one having the worst loss for instance). Since saving all the transformed volume can become a very high cost (in terme of disk space) one prefer to save the transform's parameter, and have the possibility to re-create the exact same transformed volume

    Pitch I implemented once for the RandomElasticDeformation

        def apply_given_transform(self, sample: Subject, bspline_params) -> dict:
            for image_dict in sample.get_images(intensity_only=False):
                if image_dict[TYPE] == LABEL:
                    interpolation = Interpolation.NEAREST
                else:
                    interpolation = self.interpolation
                image_dict[DATA] = self.apply_bspline_transform(
                    image_dict[DATA],
                    image_dict[AFFINE],
                    bspline_params,
                    interpolation,
                )
            return sample
    
    

    Alternatives may be a more generic way would be to separate the apply_transform code in 2 disctinct par one to get the random param one to apply them so that the apply code could be reuse in both case ....

    Additional context

    enhancement 
    opened by romainVala 55
  • How to structure queue to improve training time

    How to structure queue to improve training time

    Hi,

    I am trying to parameterize torchio.queue to improve training time. Here are my augmentations:

    global_augs_dict = {
        'normalize':ZNormalization(),
        'affine':RandomAffine(image_interpolation = 'linear'), 
        'elastic': RandomElasticDeformation(num_control_points=(7, 7, 7),locked_borders=2),
        'motion': RandomMotion(degrees=10, translation = 10, num_transforms= 2, image_interpolation = 'linear', p = 1., seed = None), 
        'ghosting': RandomGhosting(num_ghosts = (4, 10), axes = (0, 1, 2), intensity = (0.5, 1), restore = 0.02, p = 1., seed = None),
        'bias': RandomBiasField(coefficients = 0.5, order= 3, p= 1., seed = None), 
        'blur': RandomBlur(std = (0., 4.), p = 1, seed = None), 
        'noise':RandomNoise(mean = 0, std = (0, 0.25), p = 1., seed = None) , 
        'swap':RandomSwap(patch_size = 15, num_iterations = 100, p = 1, seed = None) 
    }
    

    I have tried a couple of different configurations of the queue construction (for BraTS 2020 training data):

    patches_queue = torchio.Queue(
    subjects_dataset,max_length = 1,
    samples_per_volume  = 1,
    sampler = UniformSampler([128,128,128]),
    num_workers=4,
    shuffle_subjects=False, 
    shuffle_patches=True)
    

    Here is my output:

    Output

    Hostname   : XXXXXX
    Training Data Samples:  332
    CUDA_VISIBLE_DEVICES:  0
    Current Device :  0
    Device Count on Machine :  1
    Device Name :  Tesla P100-PCIE-12GB
    Cuda Availibility :  True
    Using device: cuda
    Memory Usage:
      Allocated: 0.0 GB
      Cached:  0.0 GB
    Starting Learning rate is: 0.001
    
    
    Epoch Started at: 2020-08-20 11:07:31.169938
    Epoch # :  0
    Learning rate: 0.001
    Epoch Training dice: 0.31517219005821734
    Best Training Dice: 0.31517219005821734
    Average Training Loss: 0.6848278099417825
    Best Training Epoch:  0
    Epoch Validation dice: 0.4284125193288893
    Best Validation Dice: 0.4284125193288893
    Average Validation Loss: 0.5715874806711106
    Best Validation Epoch:  0
    Time for epoch: 730.7090194821358 mins
    
    
    Epoch Started at: 2020-08-20 23:18:13.753733
    Epoch # :  1
    Learning rate: 0.00075025
    Epoch Training dice: 0.4328200096387767
    Best Training Dice: 0.4328200096387767
    Average Training Loss: 0.5671799903612232
    Best Training Epoch:  1
    Epoch Validation dice: 0.4653519422259088
    Best Validation Dice: 0.4653519422259088
    Average Validation Loss: 0.5346480577740912
    Best Validation Epoch:  1
    Time for epoch: 727.3753080646197 mins
    
    
    Epoch Started at: 2020-08-21 11:25:36.334792
    Epoch # :  2
    Learning rate: 0.0005005
    Epoch Training dice: 0.4529779154852848
    Best Training Dice: 0.4529779154852848
    Average Training Loss: 0.5470220845147151
    Best Training Epoch:  2
    Epoch Validation dice: 0.49953905589465886
    Best Validation Dice: 0.49953905589465886
    Average Validation Loss: 0.5004609441053411
    Best Validation Epoch:  2
    Time for epoch: 738.0272558371227 mins
    
    
    Epoch Started at: 2020-08-21 23:43:38.034053
    Epoch # :  3
    Learning rate: 0.0002507499999999999
    Epoch Training dice: 0.4745635877166486
    Best Training Dice: 0.4745635877166486
    Average Training Loss: 0.5254364122833516
    Best Training Epoch:  3
    Epoch Validation dice: 0.47297200991573
    Best Validation Dice: 0.49953905589465886
    Average Validation Loss: 0.5270279900842699
    Best Validation Epoch:  2
    Time for epoch: 728.7016223390897 mins
    
    
    Epoch Started at: 2020-08-22 11:52:20.179293
    Epoch # :  4
    Learning rate: 9.999999999999159e-07
    Epoch Training dice: 0.4744484525172589
    Best Training Dice: 0.4745635877166486
    Average Training Loss: 0.5255515474827412
    Best Training Epoch:  3
    Epoch Validation dice: 0.48653238308167823
    Best Validation Dice: 0.49953905589465886
    Average Validation Loss: 0.5134676169183217
    Best Validation Epoch:  2
    Time for epoch: 724.4470616658529 mins
    
    
    Epoch Started at: 2020-08-22 23:56:47.029944
    Epoch # :  5
    Learning rate: 0.0002507499999999999
    Epoch Training dice: 0.47198090723001274
    Best Training Dice: 0.4745635877166486
    Average Training Loss: 0.5280190927699873
    Best Training Epoch:  3
    Epoch Validation dice: 0.45542112143185937
    Best Validation Dice: 0.49953905589465886
    Average Validation Loss: 0.5445788785681406
    Best Validation Epoch:  2
    Time for epoch: 723.8550246238708 mins
    
    
    Epoch Started at: 2020-08-23 12:00:38.396858
    Epoch # :  6
    Learning rate: 0.0005005
    Epoch Training dice: 0.4634351750670278
    Best Training Dice: 0.4745635877166486
    Average Training Loss: 0.5365648249329721
    Best Training Epoch:  3
    

    patches_queue = torchio.Queue(
    subjects_dataset,max_length = 10,
    samples_per_volume  = 10,
    sampler = UniformSampler([128,128,128]),
    num_workers=4,
    shuffle_subjects=False, 
    shuffle_patches=True)
    

    Here is my output:

    Output

    Hostname   : XXXXXX
    Training Data Samples:  3320
    CUDA_VISIBLE_DEVICES:  0
    Current Device :  0
    Device Count on Machine :  1
    Device Name :  Tesla P100-PCIE-12GB
    Cuda Availibility :  True
    Using device: cuda
    Memory Usage:
      Allocated: 0.0 GB
      Cached:  0.0 GB
    Starting Learning rate is: 0.001
    
    
    Epoch Started at: 2020-08-22 09:31:48.666395
    Epoch # :  0
    Learning rate: 0.001
    Epoch Training dice: 0.436339787389386
    Best Training Dice: 0.436339787389386
    Average Training Loss: 0.5636602126106139
    Best Training Epoch:  0
    Epoch Validation dice: 0.4510045856401002
    Best Validation Dice: 0.4510045856401002
    Average Validation Loss: 0.5489954143599001
    Best Validation Epoch:  0
    Time for epoch: 775.5885859568914 mins
    
    
    Epoch Started at: 2020-08-22 22:27:23.982092
    Epoch # :  1
    Learning rate: 0.00075025
    Epoch Training dice: 0.47390382915650414
    Best Training Dice: 0.47390382915650414
    Average Training Loss: 0.5260961708434958
    Best Training Epoch:  1
    Epoch Validation dice: 0.46360749397146783
    Best Validation Dice: 0.46360749397146783
    Average Validation Loss: 0.536392506028532
    Best Validation Epoch:  1
    Time for epoch: 758.6359572728475 mins
    
    
    Epoch Started at: 2020-08-23 11:06:02.140065
    Epoch # :  2
    Learning rate: 0.0005005
    Epoch Training dice: 0.4902666910009399
    Best Training Dice: 0.4902666910009399
    Average Training Loss: 0.5097333089990602
    Best Training Epoch:  2
    Epoch Validation dice: 0.4494907715524214
    Best Validation Dice: 0.46360749397146783
    Average Validation Loss: 0.5505092284475785
    Best Validation Epoch:  1
    Time for epoch: 745.3841615160306 mins
    
    
    Epoch Started at: 2020-08-23 23:31:25.190341
    Epoch # :  3
    Learning rate: 0.0002507499999999999
    Epoch Training dice: 0.501831035780201
    Best Training Dice: 0.501831035780201
    Average Training Loss: 0.498168964219799
    Best Training Epoch:  3
    

    Is there any way to improve the training time?

    Cheers, Sarthak

    patch-based-training optimization 
    opened by sarthakpati 30
  • Random simulation transform

    Random simulation transform

    Following the idea of lab2im, RandomSimulation transform simulates a volume from label maps by sampling a Gaussian variable from each label map and summing the different random variables.
    If an existing volume is given to the transform, zero elements from the simulated volume will be filled with elements of the preexisting volume.

    Results obtained from Colin27, 2008 version:

    import torch
    from torchio import RandomSimulation, DATA, RescaleIntensity, Compose
    from torchio.datasets import Colin27
    import matplotlib.pyplot as plt
    
    colin = Colin27(2008)
    torch.manual_seed(0)
    
    # Using the default coefficients
    transform = RandomSimulation(label_keys='cls')
    transformed = transform(colin)
    
    plt.figure()
    plt.imshow(transformed['image']['data'][0, :, :, 100], cmap='gray')
    plt.axis('off')
    
    # Using custom coefficients
    label_values = colin['cls'][DATA].unique()
    coefficients = {
         i: {
            'mean': i / len(label_values), 'std': 0.01
         } for i in range(1, len(label_values))
    }
    transform = RandomSimulation(label_keys='cls', coefficients=coefficients)
    transformed = transform(colin)
    
    plt.figure()
    plt.imshow(transformed['image']['data'][0, :, :, 100], cmap='gray')
    plt.axis('off')
    
    # Inpainting the simulated image on the original T1 image
    rescale_transform = RescaleIntensity((0, 1), (1, 99))   # Rescale intensity before inpainting
    simulation_transform = RandomSimulation(label_keys='cls', image_key='t1')
    transform = Compose([rescale_transform, simulation_transform])
    transformed = transform(colin)
    
    plt.figure()
    plt.imshow(transformed['t1']['data'][0, :, :, 100], cmap='gray')
    plt.axis('off')
    
    plt.show()
    

    default clean with_t1

    I don't know why but sampling the Gaussian variables is slow in Pytorch when the volume starts to be too big: image

    This kind of issue seems to have already been reported there but I haven't found a solution so far.

    opened by GFabien 29
  • Add some deterministic transforms

    Add some deterministic transforms

    Description

    This is a refactoring of the reproducibility system of the library, plus adding new features to invert transforms easily.

    Linked issues

    Resolves #191. Resolves #142. Resolves #208. Resolves #299. Resolves #336. Resolves #355.

    Further issues to take into account

    • [x] ~Saving transforms history~
    • [x] Reproducing RandomNoise (add seed to parameters?)
    • [x] ~Add a history to the Image class, and how to handle that without code duplication?~
    • [x] Think about how to handle multichannel images
    • [x] Avoid duplication of inverse() method
    • [x] Consider adding transform to history inside deterministic transforms
    • [x] Handle Compose and OneOf separately

    Progress

    New transforms

    • [x] Affine
    • [x] BiasField
    • [x] Blur
    • [x] ~Downsample~
    • [x] ElasticDeformation
    • [x] Flip
    • [x] Gamma
    • [x] Ghosting
    • [x] LabelsToImage
    • [x] Motion
    • [x] Noise
    • [x] Spike
    • [x] Swap

    To update

    • [x] Lambda
    • [x] Pad
    • [x] Crop
    • [x] Resample
    • [x] ToCanonical
    • [x] ZNormalization
    • [x] HistogramStandardization
    • [x] RescaleIntensity
    • [ ] ~CropOrPad~

    Write inverse() method

    • [x] Pad
    • [x] Crop
    • [ ] Resample?
    • [ ] ToCanonical?
    • [ ] ZNormalization?
    • [ ] HistogramStandardization?
    • [ ] RescaleIntensity?

    New usage

    import torchio as tio
    
    transforms = (
        tio.RescaleIntensity(),
        tio.RandomAffine(),
        tio.RandomBlur(),
        tio.RandomNoise(),
        tio.Flip(),
    )
    
    transform = tio.Compose(transforms)
    
    colin = tio.datasets.Colin27()
    transformed = transform(colin)
    same_transform = transformed.get_composed_history()  # reproducibility
    inverted = transformed.apply_inverse_transform()  # invertibility
    

    Checklist

    • [x] I have read the CONTRIBUTING docs and have a developer setup (especially important are pre-commitand pytest)
    • [ ] Non-breaking change (would not break existing functionality)
    • [x] Breaking change (would cause existing functionality to change)
    • [x] Tests added or modified to cover the changes
    • [x] Integration tests passed locally by running pytest
    • [x] In-line docstrings updated
    • [x] Documentation updated, tested running make html inside the docs/ folder
    • [x] This pull request is ready to be reviewed
    • [x] If the PR is ready and there are multiple commits, I have squashed them and force-pushed
    documentation enhancement help wanted good first issue tests augmentation preprocessing 
    opened by fepegar 25
  • 3D Slicer not starting after installation of torchio from ExtensionManager

    3D Slicer not starting after installation of torchio from ExtensionManager

    🐛Bug

    Slicer 4.13.0-2020-11-11

    To reproduce

    Install torchIO from the Slicer ExtensionManager. When opening it, it's asking for installing torch and torchvision which I did. Then restart Slicer

    python-traceback

    Process:               Slicer [28579]
    Path:                  /Applications/Slicer_4.13_11_12.app/Contents/MacOS/Slicer
    Identifier:            ???
    Version:               ??? (4.13.0-2020-11-11)
    Code Type:             X86-64 (Native)
    Parent Process:        ??? [1]
    Responsible:           Slicer [28579]
    User ID:               1555809845
    
    Date/Time:             2020-11-12 10:23:10.922 -0500
    OS Version:            Mac OS X 10.15.7 (19H2)
    Report Version:        12
    Bridge OS Version:     4.6 (17P6610)
    Anonymous UUID:        74F4E9BC-B2A2-9EB5-9BC5-1AEF25358764
    
    Sleep/Wake UUID:       C2118001-D5E1-4A0E-AF23-4B5DBB19B3CF
    
    Time Awake Since Boot: 260000 seconds
    Time Since Wake:       5000 seconds
    
    System Integrity Protection: enabled
    
    Crashed Thread:        0  Dispatch queue: com.apple.main-thread
    
    Exception Type:        EXC_CRASH (SIGABRT)
    Exception Codes:       0x0000000000000000, 0x0000000000000000
    Exception Note:        EXC_CORPSE_NOTIFY
    
    Application Specific Information:
    abort() called
    Slicer(28579,0x11068ddc0) malloc: *** error for object 0x7fed508293e8: pointer being freed was not allocated
     
    
    Thread 0 Crashed:: Dispatch queue: com.apple.main-thread
    0   libsystem_kernel.dylib        	0x00007fff6f35e33a __pthread_kill + 10
    1   libsystem_pthread.dylib       	0x00007fff6f41ae60 pthread_kill + 430
    2   libsystem_c.dylib             	0x00007fff6f2e5808 abort + 120
    3   libsystem_malloc.dylib        	0x00007fff6f3db50b malloc_vreport + 548
    4   libsystem_malloc.dylib        	0x00007fff6f3de40f malloc_report + 151
    5   libstdc++.6.dylib             	0x00007fff6e5111c9 std::string::assign(std::string const&) + 111
    6   _SimpleITK.cpython-36m-darwin.so	0x000000015b201fd3 0x153e61000 + 121245651
    7   _SimpleITK.cpython-36m-darwin.so	0x000000015b201d2b 0x153e61000 + 121244971
    8   _SimpleITK.cpython-36m-darwin.so	0x000000015b201786 0x153e61000 + 121243526
    9   _SimpleITK.cpython-36m-darwin.so	0x00000001547e7f4d 0x153e61000 + 9989965
    10  _SimpleITK.cpython-36m-darwin.so	0x00000001547e740a 0x153e61000 + 9987082
    11  _SimpleITK.cpython-36m-darwin.so	0x0000000154881920 0x153e61000 + 10619168
    12  dyld                          	0x00000001105de1d3 ImageLoaderMachO::doModInitFunctions(ImageLoader::LinkContext const&) + 535
    13  dyld                          	0x00000001105de5de ImageLoaderMachO::doInitialization(ImageLoader::LinkContext const&) + 40
    14  dyld                          	0x00000001105d8ffb ImageLoader::recursiveInitialization(ImageLoader::LinkContext const&, unsigned int, char const*, ImageLoader::InitializerTimingList&, ImageLoader::UninitedUpwards&) + 493
    15  dyld                          	0x00000001105d70b4 ImageLoader::processInitializers(ImageLoader::LinkContext const&, unsigned int, ImageLoader::InitializerTimingList&, ImageLoader::UninitedUpwards&) + 188
    16  dyld                          	0x00000001105d7154 ImageLoader::runInitializers(ImageLoader::LinkContext const&, ImageLoader::InitializerTimingList&) + 82
    17  dyld                          	0x00000001105c8ef2 dyld::runInitializers(ImageLoader*) + 82
    18  dyld                          	0x00000001105d2e8f dlopen_internal + 609
    19  libdyld.dylib                 	0x00007fff6f201d8a dlopen + 171
    20  libpython3.6m.dylib           	0x0000000113bd4cc9 _PyImport_FindSharedFuncptr + 297
    21  libpython3.6m.dylib           	0x0000000113c1c19f _PyImport_LoadDynamicModuleWithSpec + 511
    22  libpython3.6m.dylib           	0x0000000113c1bcd2 _imp_create_dynamic + 290
    23  libpython3.6m.dylib           	0x0000000113b6e2a6 PyCFunction_Call + 214
    24  libpython3.6m.dylib           	0x0000000113befd3b _PyEval_EvalFrameDefault + 25099
    25  libpython3.6m.dylib           	0x0000000113bf3831 _PyEval_EvalCodeWithName + 2833
    26  libpython3.6m.dylib           	0x0000000113bf3fbb fast_function + 411
    27  libpython3.6m.dylib           	0x0000000113bf2c49 call_function + 553
    28  libpython3.6m.dylib           	0x0000000113befb40 _PyEval_EvalFrameDefault + 24592
    29  libpython3.6m.dylib           	0x0000000113bf4059 fast_function + 569
    30  libpython3.6m.dylib           	0x0000000113bf2c49 call_function + 553
    31  libpython3.6m.dylib           	0x0000000113befb40 _PyEval_EvalFrameDefault + 24592
    32  libpython3.6m.dylib           	0x0000000113bf4059 fast_function + 569
    33  libpython3.6m.dylib           	0x0000000113bf2c49 call_function + 553
    34  libpython3.6m.dylib           	0x0000000113befb40 _PyEval_EvalFrameDefault + 24592
    35  libpython3.6m.dylib           	0x0000000113bf4059 fast_function + 569
    36  libpython3.6m.dylib           	0x0000000113bf2c49 call_function + 553
    37  libpython3.6m.dylib           	0x0000000113befb40 _PyEval_EvalFrameDefault + 24592
    38  libpython3.6m.dylib           	0x0000000113bf4059 fast_function + 569
    39  libpython3.6m.dylib           	0x0000000113bf2c49 call_function + 553
    40  libpython3.6m.dylib           	0x0000000113befb40 _PyEval_EvalFrameDefault + 24592
    41  libpython3.6m.dylib           	0x0000000113bf4059 fast_function + 569
    42  libpython3.6m.dylib           	0x0000000113bf2c49 call_function + 553
    43  libpython3.6m.dylib           	0x0000000113befb40 _PyEval_EvalFrameDefault + 24592
    44  libpython3.6m.dylib           	0x0000000113bf3831 _PyEval_EvalCodeWithName + 2833
    45  libpython3.6m.dylib           	0x0000000113bf3fbb fast_function + 411
    46  libpython3.6m.dylib           	0x0000000113bf2c49 call_function + 553
    47  libpython3.6m.dylib           	0x0000000113befb40 _PyEval_EvalFrameDefault + 24592
    48  libpython3.6m.dylib           	0x0000000113bf3831 _PyEval_EvalCodeWithName + 2833
    49  libpython3.6m.dylib           	0x0000000113bf3fbb fast_function + 411
    50  libpython3.6m.dylib           	0x0000000113bf2c49 call_function + 553
    51  libpython3.6m.dylib           	0x0000000113befb40 _PyEval_EvalFrameDefault + 24592
    52  libpython3.6m.dylib           	0x0000000113bf4059 fast_function + 569
    53  libpython3.6m.dylib           	0x0000000113bf2c49 call_function + 553
    54  libpython3.6m.dylib           	0x0000000113befb40 _PyEval_EvalFrameDefault + 24592
    55  libpython3.6m.dylib           	0x0000000113bf3831 _PyEval_EvalCodeWithName + 2833
    56  libpython3.6m.dylib           	0x0000000113be9aa0 PyEval_EvalCode + 48
    57  libpython3.6m.dylib           	0x0000000113be71d7 builtin_exec + 567
    58  libpython3.6m.dylib           	0x0000000113b6e2a6 PyCFunction_Call + 214
    59  libpython3.6m.dylib           	0x0000000113befd3b _PyEval_EvalFrameDefault + 25099
    60  libpython3.6m.dylib           	0x0000000113bf3831 _PyEval_EvalCodeWithName + 2833
    61  libpython3.6m.dylib           	0x0000000113bf3fbb fast_function + 411
    62  libpython3.6m.dylib           	0x0000000113bf2c49 call_function + 553
    63  libpython3.6m.dylib           	0x0000000113befb40 _PyEval_EvalFrameDefault + 24592
    64  libpython3.6m.dylib           	0x0000000113bf4059 fast_function + 569
    65  libpython3.6m.dylib           	0x0000000113bf2c49 call_function + 553
    66  libpython3.6m.dylib           	0x0000000113befb40 _PyEval_EvalFrameDefault + 24592
    67  libpython3.6m.dylib           	0x0000000113bf4059 fast_function + 569
    68  libpython3.6m.dylib           	0x0000000113bf2c49 call_function + 553
    69  libpython3.6m.dylib           	0x0000000113befb40 _PyEval_EvalFrameDefault + 24592
    70  libpython3.6m.dylib           	0x0000000113bf4059 fast_function + 569
    71  libpython3.6m.dylib           	0x0000000113bf2c49 call_function + 553
    72  libpython3.6m.dylib           	0x0000000113befb40 _PyEval_EvalFrameDefault + 24592
    73  libpython3.6m.dylib           	0x0000000113bf4507 _PyFunction_FastCallDict + 951
    74  libpython3.6m.dylib           	0x0000000113b2b45f _PyObject_FastCallDict + 191
    75  libpython3.6m.dylib           	0x0000000113b2cb5f _PyObject_CallMethodIdObjArgs + 767
    76  libpython3.6m.dylib           	0x0000000113c1ad43 PyImport_ImportModuleLevelObject + 1251
    77  libpython3.6m.dylib           	0x0000000113bee625 _PyEval_EvalFrameDefault + 19189
    78  libpython3.6m.dylib           	0x0000000113bf3831 _PyEval_EvalCodeWithName + 2833
    79  libpython3.6m.dylib           	0x0000000113be9aa0 PyEval_EvalCode + 48
    80  libpython3.6m.dylib           	0x0000000113be71d7 builtin_exec + 567
    81  libpython3.6m.dylib           	0x0000000113b6e2a6 PyCFunction_Call + 214
    82  libpython3.6m.dylib           	0x0000000113befd3b _PyEval_EvalFrameDefault + 25099
    83  libpython3.6m.dylib           	0x0000000113bf3831 _PyEval_EvalCodeWithName + 2833
    84  libpython3.6m.dylib           	0x0000000113bf3fbb fast_function + 411
    85  libpython3.6m.dylib           	0x0000000113bf2c49 call_function + 553
    86  libpython3.6m.dylib           	0x0000000113befb40 _PyEval_EvalFrameDefault + 24592
    87  libpython3.6m.dylib           	0x0000000113bf4059 fast_function + 569
    88  libpython3.6m.dylib           	0x0000000113bf2c49 call_function + 553
    89  libpython3.6m.dylib           	0x0000000113befb40 _PyEval_EvalFrameDefault + 24592
    90  libpython3.6m.dylib           	0x0000000113bf4059 fast_function + 569
    91  libpython3.6m.dylib           	0x0000000113bf2c49 call_function + 553
    92  libpython3.6m.dylib           	0x0000000113befb40 _PyEval_EvalFrameDefault + 24592
    93  libpython3.6m.dylib           	0x0000000113bf4059 fast_function + 569
    94  libpython3.6m.dylib           	0x0000000113bf2c49 call_function + 553
    95  libpython3.6m.dylib           	0x0000000113befb40 _PyEval_EvalFrameDefault + 24592
    96  libpython3.6m.dylib           	0x0000000113bf4507 _PyFunction_FastCallDict + 951
    97  libpython3.6m.dylib           	0x0000000113b2b45f _PyObject_FastCallDict + 191
    98  libpython3.6m.dylib           	0x0000000113b2cb5f _PyObject_CallMethodIdObjArgs + 767
    99  libpython3.6m.dylib           	0x0000000113c1ad43 PyImport_ImportModuleLevelObject + 1251
    100 libpython3.6m.dylib           	0x0000000113bee625 _PyEval_EvalFrameDefault + 19189
    101 libpython3.6m.dylib           	0x0000000113bf3831 _PyEval_EvalCodeWithName + 2833
    102 libpython3.6m.dylib           	0x0000000113be9aa0 PyEval_EvalCode + 48
    103 libpython3.6m.dylib           	0x0000000113be71d7 builtin_exec + 567
    104 libpython3.6m.dylib           	0x0000000113b6e2a6 PyCFunction_Call + 214
    105 libpython3.6m.dylib           	0x0000000113befd3b _PyEval_EvalFrameDefault + 25099
    106 libpython3.6m.dylib           	0x0000000113bf3831 _PyEval_EvalCodeWithName + 2833
    107 libpython3.6m.dylib           	0x0000000113bf3fbb fast_function + 411
    108 libpython3.6m.dylib           	0x0000000113bf2c49 call_function + 553
    109 libpython3.6m.dylib           	0x0000000113befb40 _PyEval_EvalFrameDefault + 24592
    110 libpython3.6m.dylib           	0x0000000113bf4059 fast_function + 569
    111 libpython3.6m.dylib           	0x0000000113bf2c49 call_function + 553
    112 libpython3.6m.dylib           	0x0000000113befb40 _PyEval_EvalFrameDefault + 24592
    113 libpython3.6m.dylib           	0x0000000113bf4059 fast_function + 569
    114 libpython3.6m.dylib           	0x0000000113bf2c49 call_function + 553
    115 libpython3.6m.dylib           	0x0000000113befb40 _PyEval_EvalFrameDefault + 24592
    116 libpython3.6m.dylib           	0x0000000113bf3831 _PyEval_EvalCodeWithName + 2833
    117 libpython3.6m.dylib           	0x0000000113bf3fbb fast_function + 411
    118 libpython3.6m.dylib           	0x0000000113bf2c49 call_function + 553
    119 libpython3.6m.dylib           	0x0000000113befb40 _PyEval_EvalFrameDefault + 24592
    120 libpython3.6m.dylib           	0x0000000113bf3831 _PyEval_EvalCodeWithName + 2833
    121 libpython3.6m.dylib           	0x0000000113be9aa0 PyEval_EvalCode + 48
    122 libpython3.6m.dylib           	0x0000000113c4079d PyRun_StringFlags + 141
    123 libqSlicerBaseQTCore.dylib    	0x0000000103ac8af5 qSlicerScriptedUtils::loadSourceAsModule(QString const&, QString const&, _object*, _object*) + 309
    124 libqSlicerBaseQTGUI.dylib     	0x00000001031d2b4d qSlicerScriptedLoadableModule::setPythonSource(QString const&) + 573
    125 libqSlicerBaseQTGUI.dylib     	0x00000001031ca382 ctkFactoryScriptedItem::instanciator() + 418
    126 libqSlicerBaseQTGUI.dylib     	0x00000001031cbc21 ctkAbstractFactory<qSlicerAbstractCoreModule>::instantiate(QString const&) + 193
    127 libqSlicerBaseQTCore.dylib    	0x0000000103a644c9 qSlicerAbstractModuleFactoryManager::instantiateModule(QString const&) + 57
    128 libqSlicerBaseQTCore.dylib    	0x0000000103a6422b qSlicerAbstractModuleFactoryManager::instantiateModules() + 235
    129                               	0x0000000102e1b0e3 int qSlicerApplicationHelper::postInitializeApplication<qSlicerAppMainWindow>(qSlicerApplication&, QScopedPointer<QSplashScreen, QScopedPointerDeleter<QSplashScreen> >&, QScopedPointer<qSlicerAppMainWindow, QScopedPointerDeleter<qSlicerAppMainWindow> >&) + 2851
    130                               	0x0000000102e1a2d0 main + 144
    131 libdyld.dylib                 	0x00007fff6f216cc9 start + 1
    
    Thread 1:
    0   libsystem_pthread.dylib       	0x00007fff6f416b68 start_wqthread + 0
    
    Thread 2:
    0   libsystem_pthread.dylib       	0x00007fff6f416b68 start_wqthread + 0
    
    Thread 3:
    0   libsystem_pthread.dylib       	0x00007fff6f416b68 start_wqthread + 0
    
    Thread 4:
    0   libsystem_pthread.dylib       	0x00007fff6f416b68 start_wqthread + 0
    
    Thread 5:
    0   libsystem_kernel.dylib        	0x00007fff6f35a756 __semwait_signal + 10
    1   libsystem_c.dylib             	0x00007fff6f2ddeea nanosleep + 196
    2   libsystem_c.dylib             	0x00007fff6f2ddde4 usleep + 53
    3   libSlicerBaseLogic.dylib      	0x000000010de16500 vtkSlicerApplicationLogic::ProcessProcessingTasks() + 320
    4   libSlicerBaseLogic.dylib      	0x000000010de16136 vtkSlicerApplicationLogic::ProcessingThreaderCallback(void*) + 38
    5   libsystem_pthread.dylib       	0x00007fff6f41b109 _pthread_start + 148
    6   libsystem_pthread.dylib       	0x00007fff6f416b8b thread_start + 15
    
    Thread 6:
    0   libsystem_kernel.dylib        	0x00007fff6f35a756 __semwait_signal + 10
    1   libsystem_c.dylib             	0x00007fff6f2ddeea nanosleep + 196
    2   libsystem_c.dylib             	0x00007fff6f2ddde4 usleep + 53
    3   libSlicerBaseLogic.dylib      	0x000000010de16680 vtkSlicerApplicationLogic::ProcessNetworkingTasks() + 320
    4   libSlicerBaseLogic.dylib      	0x000000010de16206 vtkSlicerApplicationLogic::NetworkingThreaderCallback(void*) + 38
    5   libsystem_pthread.dylib       	0x00007fff6f41b109 _pthread_start + 148
    6   libsystem_pthread.dylib       	0x00007fff6f416b8b thread_start + 15
    
    Thread 7:: Qt bearer thread
    0   libsystem_kernel.dylib        	0x00007fff6f35e3d6 poll + 10
    1   org.qt-project.QtCore         	0x0000000104a64fe0 qt_safe_poll(pollfd*, unsigned int, timespec const*) + 608
    2   org.qt-project.QtCore         	0x0000000104a667e1 QEventDispatcherUNIX::processEvents(QFlags<QEventLoop::ProcessEventsFlag>) + 849
    3   org.qt-project.QtCore         	0x00000001049fe88f QEventLoop::exec(QFlags<QEventLoop::ProcessEventsFlag>) + 431
    4   org.qt-project.QtCore         	0x000000010482b793 QThread::exec() + 131
    5   org.qt-project.QtCore         	0x000000010482c7a9 0x10480a000 + 141225
    6   libsystem_pthread.dylib       	0x00007fff6f41b109 _pthread_start + 148
    7   libsystem_pthread.dylib       	0x00007fff6f416b8b thread_start + 15
    
    Thread 8:: ctkFDHandler
    0   libsystem_kernel.dylib        	0x00007fff6f35881e read + 10
    1   libCTKCore.0.1.dylib          	0x000000010fd76c21 ctkFDHandler::run() + 113
    2   org.qt-project.QtCore         	0x000000010482c7a9 0x10480a000 + 141225
    3   libsystem_pthread.dylib       	0x00007fff6f41b109 _pthread_start + 148
    4   libsystem_pthread.dylib       	0x00007fff6f416b8b thread_start + 15
    
    Thread 9:: ctkFDHandler
    0   libsystem_kernel.dylib        	0x00007fff6f35881e read + 10
    1   libCTKCore.0.1.dylib          	0x000000010fd76c21 ctkFDHandler::run() + 113
    2   org.qt-project.QtCore         	0x000000010482c7a9 0x10480a000 + 141225
    3   libsystem_pthread.dylib       	0x00007fff6f41b109 _pthread_start + 148
    4   libsystem_pthread.dylib       	0x00007fff6f416b8b thread_start + 15
    
    Thread 10:
    0   libsystem_pthread.dylib       	0x00007fff6f416b68 start_wqthread + 0
    
    Thread 11:
    0   libsystem_pthread.dylib       	0x00007fff6f416b68 start_wqthread + 0
    
    Thread 12:
    0   libsystem_pthread.dylib       	0x00007fff6f416b68 start_wqthread + 0
    
    Thread 13:
    0   libsystem_pthread.dylib       	0x00007fff6f416b68 start_wqthread + 0
    
    Thread 14:
    0   libsystem_pthread.dylib       	0x00007fff6f416b68 start_wqthread + 0
    
    Thread 15:
    0   libsystem_pthread.dylib       	0x00007fff6f416b68 start_wqthread + 0
    
    Thread 16:
    0   libsystem_pthread.dylib       	0x00007fff6f416b68 start_wqthread + 0
    
    Thread 17:
    0   libsystem_pthread.dylib       	0x00007fff6f416b68 start_wqthread + 0
    
    Thread 18:
    0   libsystem_pthread.dylib       	0x00007fff6f416b68 start_wqthread + 0
    
    Thread 19:: com.apple.NSEventThread
    0   libsystem_kernel.dylib        	0x00007fff6f357dfa mach_msg_trap + 10
    1   libsystem_kernel.dylib        	0x00007fff6f358170 mach_msg + 60
    2   com.apple.CoreFoundation      	0x00007fff351a6ef5 __CFRunLoopServiceMachPort + 247
    3   com.apple.CoreFoundation      	0x00007fff351a59c2 __CFRunLoopRun + 1319
    4   com.apple.CoreFoundation      	0x00007fff351a4e3e CFRunLoopRunSpecific + 462
    5   com.apple.AppKit              	0x00007fff325b8954 _NSEventThread + 132
    6   libsystem_pthread.dylib       	0x00007fff6f41b109 _pthread_start + 148
    7   libsystem_pthread.dylib       	0x00007fff6f416b8b thread_start + 15
    
    Thread 20:
    0   libsystem_kernel.dylib        	0x00007fff6f35a882 __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff6f41b425 _pthread_cond_wait + 698
    2   libopenblas.0.dylib           	0x000000013e8b327b blas_thread_server + 507
    3   libsystem_pthread.dylib       	0x00007fff6f41b109 _pthread_start + 148
    4   libsystem_pthread.dylib       	0x00007fff6f416b8b thread_start + 15
    
    Thread 21:
    0   libsystem_kernel.dylib        	0x00007fff6f35a882 __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff6f41b425 _pthread_cond_wait + 698
    2   libopenblas.0.dylib           	0x000000013e8b327b blas_thread_server + 507
    3   libsystem_pthread.dylib       	0x00007fff6f41b109 _pthread_start + 148
    4   libsystem_pthread.dylib       	0x00007fff6f416b8b thread_start + 15
    
    Thread 22:
    0   libsystem_kernel.dylib        	0x00007fff6f35a882 __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff6f41b425 _pthread_cond_wait + 698
    2   libopenblas.0.dylib           	0x000000013e8b327b blas_thread_server + 507
    3   libsystem_pthread.dylib       	0x00007fff6f41b109 _pthread_start + 148
    4   libsystem_pthread.dylib       	0x00007fff6f416b8b thread_start + 15
    
    Thread 23:
    0   libsystem_kernel.dylib        	0x00007fff6f35a882 __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff6f41b425 _pthread_cond_wait + 698
    2   libopenblas.0.dylib           	0x000000013e8b327b blas_thread_server + 507
    3   libsystem_pthread.dylib       	0x00007fff6f41b109 _pthread_start + 148
    4   libsystem_pthread.dylib       	0x00007fff6f416b8b thread_start + 15
    
    Thread 24:
    0   libsystem_kernel.dylib        	0x00007fff6f35a882 __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff6f41b425 _pthread_cond_wait + 698
    2   libopenblas.0.dylib           	0x000000013e8b327b blas_thread_server + 507
    3   libsystem_pthread.dylib       	0x00007fff6f41b109 _pthread_start + 148
    4   libsystem_pthread.dylib       	0x00007fff6f416b8b thread_start + 15
    
    Thread 25:
    0   libsystem_kernel.dylib        	0x00007fff6f35a882 __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff6f41b425 _pthread_cond_wait + 698
    2   libopenblas.0.dylib           	0x000000013e8b327b blas_thread_server + 507
    3   libsystem_pthread.dylib       	0x00007fff6f41b109 _pthread_start + 148
    4   libsystem_pthread.dylib       	0x00007fff6f416b8b thread_start + 15
    
    Thread 26:
    0   libsystem_kernel.dylib        	0x00007fff6f35a882 __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff6f41b425 _pthread_cond_wait + 698
    2   libopenblas.0.dylib           	0x000000013e8b327b blas_thread_server + 507
    3   libsystem_pthread.dylib       	0x00007fff6f41b109 _pthread_start + 148
    4   libsystem_pthread.dylib       	0x00007fff6f416b8b thread_start + 15
    
    Thread 27:: Thread (pooled)
    0   libsystem_kernel.dylib        	0x00007fff6f35a882 __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff6f41b425 _pthread_cond_wait + 698
    2   org.qt-project.QtCore         	0x0000000104834c7f 0x10480a000 + 175231
    3   org.qt-project.QtCore         	0x000000010483494e 0x10480a000 + 174414
    4   org.qt-project.QtCore         	0x000000010483486d QWaitCondition::wait(QMutex*, QDeadlineTimer) + 93
    5   org.qt-project.QtCore         	0x0000000104830e5d 0x10480a000 + 159325
    6   org.qt-project.QtCore         	0x000000010482c7a9 0x10480a000 + 141225
    7   libsystem_pthread.dylib       	0x00007fff6f41b109 _pthread_start + 148
    8   libsystem_pthread.dylib       	0x00007fff6f416b8b thread_start + 15
    
    Thread 28:: Thread (pooled)
    0   libsystem_kernel.dylib        	0x00007fff6f35a882 __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff6f41b425 _pthread_cond_wait + 698
    2   org.qt-project.QtCore         	0x0000000104834c7f 0x10480a000 + 175231
    3   org.qt-project.QtCore         	0x000000010483494e 0x10480a000 + 174414
    4   org.qt-project.QtCore         	0x000000010483486d QWaitCondition::wait(QMutex*, QDeadlineTimer) + 93
    5   org.qt-project.QtCore         	0x0000000104830e5d 0x10480a000 + 159325
    6   org.qt-project.QtCore         	0x000000010482c7a9 0x10480a000 + 141225
    7   libsystem_pthread.dylib       	0x00007fff6f41b109 _pthread_start + 148
    8   libsystem_pthread.dylib       	0x00007fff6f416b8b thread_start + 15
    
    Thread 29:: Thread (pooled)
    0   libsystem_kernel.dylib        	0x00007fff6f35a882 __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff6f41b425 _pthread_cond_wait + 698
    2   org.qt-project.QtCore         	0x0000000104834c7f 0x10480a000 + 175231
    3   org.qt-project.QtCore         	0x000000010483494e 0x10480a000 + 174414
    4   org.qt-project.QtCore         	0x000000010483486d QWaitCondition::wait(QMutex*, QDeadlineTimer) + 93
    5   org.qt-project.QtCore         	0x0000000104830e5d 0x10480a000 + 159325
    6   org.qt-project.QtCore         	0x000000010482c7a9 0x10480a000 + 141225
    7   libsystem_pthread.dylib       	0x00007fff6f41b109 _pthread_start + 148
    8   libsystem_pthread.dylib       	0x00007fff6f416b8b thread_start + 15
    
    Thread 30:: Thread (pooled)
    0   libsystem_kernel.dylib        	0x00007fff6f35a882 __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff6f41b425 _pthread_cond_wait + 698
    2   org.qt-project.QtCore         	0x0000000104834c7f 0x10480a000 + 175231
    3   org.qt-project.QtCore         	0x000000010483494e 0x10480a000 + 174414
    4   org.qt-project.QtCore         	0x000000010483486d QWaitCondition::wait(QMutex*, QDeadlineTimer) + 93
    5   org.qt-project.QtCore         	0x0000000104830e5d 0x10480a000 + 159325
    6   org.qt-project.QtCore         	0x000000010482c7a9 0x10480a000 + 141225
    7   libsystem_pthread.dylib       	0x00007fff6f41b109 _pthread_start + 148
    8   libsystem_pthread.dylib       	0x00007fff6f416b8b thread_start + 15
    
    Thread 31:: Thread (pooled)
    0   libsystem_kernel.dylib        	0x00007fff6f35a882 __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff6f41b425 _pthread_cond_wait + 698
    2   org.qt-project.QtCore         	0x0000000104834c7f 0x10480a000 + 175231
    3   org.qt-project.QtCore         	0x000000010483494e 0x10480a000 + 174414
    4   org.qt-project.QtCore         	0x000000010483486d QWaitCondition::wait(QMutex*, QDeadlineTimer) + 93
    5   org.qt-project.QtCore         	0x0000000104830e5d 0x10480a000 + 159325
    6   org.qt-project.QtCore         	0x000000010482c7a9 0x10480a000 + 141225
    7   libsystem_pthread.dylib       	0x00007fff6f41b109 _pthread_start + 148
    8   libsystem_pthread.dylib       	0x00007fff6f416b8b thread_start + 15
    
    Thread 32:: Thread (pooled)
    0   libsystem_kernel.dylib        	0x00007fff6f35a882 __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff6f41b425 _pthread_cond_wait + 698
    2   org.qt-project.QtCore         	0x0000000104834c7f 0x10480a000 + 175231
    3   org.qt-project.QtCore         	0x000000010483494e 0x10480a000 + 174414
    4   org.qt-project.QtCore         	0x000000010483486d QWaitCondition::wait(QMutex*, QDeadlineTimer) + 93
    5   org.qt-project.QtCore         	0x0000000104830e5d 0x10480a000 + 159325
    6   org.qt-project.QtCore         	0x000000010482c7a9 0x10480a000 + 141225
    7   libsystem_pthread.dylib       	0x00007fff6f41b109 _pthread_start + 148
    8   libsystem_pthread.dylib       	0x00007fff6f416b8b thread_start + 15
    
    Thread 33:: Thread (pooled)
    0   libsystem_kernel.dylib        	0x00007fff6f35a882 __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff6f41b425 _pthread_cond_wait + 698
    2   org.qt-project.QtCore         	0x0000000104834c7f 0x10480a000 + 175231
    3   org.qt-project.QtCore         	0x000000010483494e 0x10480a000 + 174414
    4   org.qt-project.QtCore         	0x000000010483486d QWaitCondition::wait(QMutex*, QDeadlineTimer) + 93
    5   org.qt-project.QtCore         	0x0000000104830e5d 0x10480a000 + 159325
    6   org.qt-project.QtCore         	0x000000010482c7a9 0x10480a000 + 141225
    7   libsystem_pthread.dylib       	0x00007fff6f41b109 _pthread_start + 148
    8   libsystem_pthread.dylib       	0x00007fff6f416b8b thread_start + 15
    
    Thread 34:: Thread (pooled)
    0   libsystem_kernel.dylib        	0x00007fff6f35a882 __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff6f41b425 _pthread_cond_wait + 698
    2   org.qt-project.QtCore         	0x0000000104834c7f 0x10480a000 + 175231
    3   org.qt-project.QtCore         	0x000000010483494e 0x10480a000 + 174414
    4   org.qt-project.QtCore         	0x000000010483486d QWaitCondition::wait(QMutex*, QDeadlineTimer) + 93
    5   org.qt-project.QtCore         	0x0000000104830e5d 0x10480a000 + 159325
    6   org.qt-project.QtCore         	0x000000010482c7a9 0x10480a000 + 141225
    7   libsystem_pthread.dylib       	0x00007fff6f41b109 _pthread_start + 148
    8   libsystem_pthread.dylib       	0x00007fff6f416b8b thread_start + 15
    
    Thread 35:: Thread (pooled)
    0   libsystem_kernel.dylib        	0x00007fff6f35a882 __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff6f41b425 _pthread_cond_wait + 698
    2   org.qt-project.QtCore         	0x0000000104834c7f 0x10480a000 + 175231
    3   org.qt-project.QtCore         	0x000000010483494e 0x10480a000 + 174414
    4   org.qt-project.QtCore         	0x000000010483486d QWaitCondition::wait(QMutex*, QDeadlineTimer) + 93
    5   org.qt-project.QtCore         	0x0000000104830e5d 0x10480a000 + 159325
    6   org.qt-project.QtCore         	0x000000010482c7a9 0x10480a000 + 141225
    7   libsystem_pthread.dylib       	0x00007fff6f41b109 _pthread_start + 148
    8   libsystem_pthread.dylib       	0x00007fff6f416b8b thread_start + 15
    
    Thread 36:: Thread (pooled)
    0   libsystem_kernel.dylib        	0x00007fff6f35a882 __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff6f41b425 _pthread_cond_wait + 698
    2   org.qt-project.QtCore         	0x0000000104834c7f 0x10480a000 + 175231
    3   org.qt-project.QtCore         	0x000000010483494e 0x10480a000 + 174414
    4   org.qt-project.QtCore         	0x000000010483486d QWaitCondition::wait(QMutex*, QDeadlineTimer) + 93
    5   org.qt-project.QtCore         	0x0000000104830e5d 0x10480a000 + 159325
    6   org.qt-project.QtCore         	0x000000010482c7a9 0x10480a000 + 141225
    7   libsystem_pthread.dylib       	0x00007fff6f41b109 _pthread_start + 148
    8   libsystem_pthread.dylib       	0x00007fff6f416b8b thread_start + 15
    
    Thread 37:: Thread (pooled)
    0   libsystem_kernel.dylib        	0x00007fff6f35a882 __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff6f41b425 _pthread_cond_wait + 698
    2   org.qt-project.QtCore         	0x0000000104834c7f 0x10480a000 + 175231
    3   org.qt-project.QtCore         	0x000000010483494e 0x10480a000 + 174414
    4   org.qt-project.QtCore         	0x000000010483486d QWaitCondition::wait(QMutex*, QDeadlineTimer) + 93
    5   org.qt-project.QtCore         	0x0000000104830e5d 0x10480a000 + 159325
    6   org.qt-project.QtCore         	0x000000010482c7a9 0x10480a000 + 141225
    7   libsystem_pthread.dylib       	0x00007fff6f41b109 _pthread_start + 148
    8   libsystem_pthread.dylib       	0x00007fff6f416b8b thread_start + 15
    
    Thread 38:: Thread (pooled)
    0   libsystem_kernel.dylib        	0x00007fff6f35a882 __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff6f41b425 _pthread_cond_wait + 698
    2   org.qt-project.QtCore         	0x0000000104834c7f 0x10480a000 + 175231
    3   org.qt-project.QtCore         	0x000000010483494e 0x10480a000 + 174414
    4   org.qt-project.QtCore         	0x000000010483486d QWaitCondition::wait(QMutex*, QDeadlineTimer) + 93
    5   org.qt-project.QtCore         	0x0000000104830e5d 0x10480a000 + 159325
    6   org.qt-project.QtCore         	0x000000010482c7a9 0x10480a000 + 141225
    7   libsystem_pthread.dylib       	0x00007fff6f41b109 _pthread_start + 148
    8   libsystem_pthread.dylib       	0x00007fff6f416b8b thread_start + 15
    
    Thread 39:: Thread (pooled)
    0   libsystem_kernel.dylib        	0x00007fff6f35a882 __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff6f41b425 _pthread_cond_wait + 698
    2   org.qt-project.QtCore         	0x0000000104834c7f 0x10480a000 + 175231
    3   org.qt-project.QtCore         	0x000000010483494e 0x10480a000 + 174414
    4   org.qt-project.QtCore         	0x000000010483486d QWaitCondition::wait(QMutex*, QDeadlineTimer) + 93
    5   org.qt-project.QtCore         	0x0000000104830e5d 0x10480a000 + 159325
    6   org.qt-project.QtCore         	0x000000010482c7a9 0x10480a000 + 141225
    7   libsystem_pthread.dylib       	0x00007fff6f41b109 _pthread_start + 148
    8   libsystem_pthread.dylib       	0x00007fff6f416b8b thread_start + 15
    
    Thread 40:: Thread (pooled)
    0   libsystem_kernel.dylib        	0x00007fff6f35a882 __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff6f41b425 _pthread_cond_wait + 698
    2   org.qt-project.QtCore         	0x0000000104834c7f 0x10480a000 + 175231
    3   org.qt-project.QtCore         	0x000000010483494e 0x10480a000 + 174414
    4   org.qt-project.QtCore         	0x000000010483486d QWaitCondition::wait(QMutex*, QDeadlineTimer) + 93
    5   org.qt-project.QtCore         	0x0000000104830e5d 0x10480a000 + 159325
    6   org.qt-project.QtCore         	0x000000010482c7a9 0x10480a000 + 141225
    7   libsystem_pthread.dylib       	0x00007fff6f41b109 _pthread_start + 148
    8   libsystem_pthread.dylib       	0x00007fff6f416b8b thread_start + 15
    
    Thread 41:: Thread (pooled)
    0   libsystem_kernel.dylib        	0x00007fff6f35a882 __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff6f41b425 _pthread_cond_wait + 698
    2   org.qt-project.QtCore         	0x0000000104834c7f 0x10480a000 + 175231
    3   org.qt-project.QtCore         	0x000000010483494e 0x10480a000 + 174414
    4   org.qt-project.QtCore         	0x000000010483486d QWaitCondition::wait(QMutex*, QDeadlineTimer) + 93
    5   org.qt-project.QtCore         	0x0000000104830e5d 0x10480a000 + 159325
    6   org.qt-project.QtCore         	0x000000010482c7a9 0x10480a000 + 141225
    7   libsystem_pthread.dylib       	0x00007fff6f41b109 _pthread_start + 148
    8   libsystem_pthread.dylib       	0x00007fff6f416b8b thread_start + 15
    
    Thread 42:: Thread (pooled)
    0   libsystem_kernel.dylib        	0x00007fff6f35a882 __psynch_cvwait + 10
    1   libsystem_pthread.dylib       	0x00007fff6f41b425 _pthread_cond_wait + 698
    2   org.qt-project.QtCore         	0x0000000104834c7f 0x10480a000 + 175231
    3   org.qt-project.QtCore         	0x000000010483494e 0x10480a000 + 174414
    4   org.qt-project.QtCore         	0x000000010483486d QWaitCondition::wait(QMutex*, QDeadlineTimer) + 93
    5   org.qt-project.QtCore         	0x0000000104830e5d 0x10480a000 + 159325
    6   org.qt-project.QtCore         	0x000000010482c7a9 0x10480a000 + 141225
    7   libsystem_pthread.dylib       	0x00007fff6f41b109 _pthread_start + 148
    8   libsystem_pthread.dylib       	0x00007fff6f416b8b thread_start + 15
    
    Thread 0 crashed with X86 Thread State (64-bit):
      rax: 0x0000000000000000  rbx: 0x000000011068ddc0  rcx: 0x00007ffeece0d0f8  rdx: 0x0000000000000000
      rdi: 0x0000000000000307  rsi: 0x0000000000000006  rbp: 0x00007ffeece0d120  rsp: 0x00007ffeece0d0f8
       r8: 0x0000000000000000   r9: 0x0000000000000000  r10: 0x000000011068ddc0  r11: 0x0000000000000246
      r12: 0x0000000000000307  r13: 0x0000000000000050  r14: 0x0000000000000006  r15: 0x0000000000000016
      rip: 0x00007fff6f35e33a  rfl: 0x0000000000000246  cr2: 0x00000001368f4000
      
    Logical CPU:     0
    Error Code:      0x02000148
    Trap Number:     133
    
    
    Binary Images:
           0x102de2000 -        0x102e1effb + (??? - 4.13.0-2020-11-11) <93245227-0F64-3921-BC46-F47DEB658532> /Applications/Slicer_4.13_11_12.app/Contents/MacOS/Slicer
           0x102e24000 -        0x102e25ff7 +libITKSmoothing-5.1.1.dylib (0) <BD1CB53B-2772-3D43-A971-7DE7D1AC1A2F> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libITKSmoothing-5.1.1.dylib
           0x102e2a000 -        0x102eefffb +libqSlicerApp.dylib (0) <7B97ECB4-CF3E-3817-B468-10AE7360A5EE> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libqSlicerApp.dylib
           0x102ef8000 -        0x102efafff +libITKVNLInstantiation-5.1.1.dylib (0) <1221D624-3269-3C6A-8DB4-71AAA6EA5514> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libITKVNLInstantiation-5.1.1.dylib
           0x102efe000 -        0x102f8effb +libqSlicerBaseQTApp.dylib (0) <9152ABB2-664D-3A4D-990B-2BA60400008F> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libqSlicerBaseQTApp.dylib
           0x102fa9000 -        0x10301aff3 +libqSlicerModulesCore.dylib (0) <A42F31A4-D161-3DAE-A6B9-A5CCC4308969> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libqSlicerModulesCore.dylib
           0x10302b000 -        0x1030deff3 +libqSlicerBaseQTCLI.dylib (0) <33942052-80C6-326C-B50F-C3270322DA06> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libqSlicerBaseQTCLI.dylib
           0x10310d000 -        0x10310eff7 +libitkvcl-5.1.1.dylib (0) <3195C810-FE1E-3546-B6B4-C59BF5ED4AD1> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libitkvcl-5.1.1.dylib
           0x103113000 -        0x103313ff7 +libqSlicerBaseQTGUI.dylib (0) <B3FB9027-94C3-301E-B8BF-04A0A7DC12A5> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libqSlicerBaseQTGUI.dylib
           0x10339b000 -        0x10339fffb +libITKRegionGrowing-5.1.1.dylib (0) <187ED7C4-A78D-327E-AE65-D048971B8AD3> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libITKRegionGrowing-5.1.1.dylib
           0x1033a2000 -        0x103579fff +libqMRMLWidgets.dylib (0) <BAE8D244-4EF3-3521-98D1-1587A5ADAB55> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libqMRMLWidgets.dylib
           0x10364b000 -        0x10364cffb +libitkgdcmuuid-5.1.1.dylib (0) <A38E7C04-CCA3-31F4-9D8F-9645DE79DB3D> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libitkgdcmuuid-5.1.1.dylib
           0x10364f000 -        0x103668ff3 +libCTKQtTesting.0.1.dylib (0) <D84AF970-8FF0-307E-9A54-7D9F74DBA23E> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libCTKQtTesting.0.1.dylib
           0x103675000 -        0x1036c7ff3 +libCTKVisualizationVTKWidgets.0.1.dylib (0) <F26B958A-0992-33DC-B96A-46BF09F7DE1D> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libCTKVisualizationVTKWidgets.0.1.dylib
           0x103711000 -        0x10371dff7 +libCTKScriptingPythonWidgets.0.1.dylib (0) <35EA98C6-EF49-39D9-A87B-15E42246F7A5> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libCTKScriptingPythonWidgets.0.1.dylib
           0x10372b000 -        0x10378eff7 +libCTKDICOMWidgets.0.1.dylib (0) <4C327E9A-560C-3FBD-A9D1-826707592B72> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libCTKDICOMWidgets.0.1.dylib
           0x1037c5000 -        0x1037c9ff3 +libitkNetlibSlatec-5.1.1.dylib (0) <561A71AD-2ACA-38A3-BD27-043D2BB7B05D> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libitkNetlibSlatec-5.1.1.dylib
           0x1037cc000 -        0x1038e2fff +libCTKWidgets.0.1.dylib (0) <2B2BDAFA-CE95-3862-A369-675E621D544D> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libCTKWidgets.0.1.dylib
           0x1039bb000 -        0x1039fbffb +libQtTesting.dylib (0) <156D9D70-E1D5-3BE8-8074-A8253F9926CD> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libQtTesting.dylib
           0x103a29000 -        0x103b3bfff +libqSlicerBaseQTCore.dylib (0) <713F3321-1B38-3279-8FEE-1ED53FD177CB> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libqSlicerBaseQTCore.dylib
           0x103b8a000 -        0x103c04fe7 +org.qt-project.QtMultimedia (5.15 - 5.15.1) <1837D0AD-DADA-35ED-B186-CA4B1688CCC1> /Applications/Slicer_4.13_11_12.app/Contents/Frameworks/QtMultimedia.framework/Versions/5/QtMultimedia
           0x103c59000 -        0x10409ffff +org.qt-project.QtWidgets (5.15 - 5.15.1) <0E55C983-CDCD-363A-95B3-13A2D857D999> /Applications/Slicer_4.13_11_12.app/Contents/Frameworks/QtWidgets.framework/Versions/5/QtWidgets
           0x104206000 -        0x1046eeff3 +org.qt-project.QtGui (5.15 - 5.15.1) <0CB5AA76-B550-36E8-98D1-025FA9EE0147> /Applications/Slicer_4.13_11_12.app/Contents/Frameworks/QtGui.framework/Versions/5/QtGui
           0x10480a000 -        0x104d79727 +org.qt-project.QtCore (5.15 - 5.15.1) <A8D9E499-21E6-3822-BA3C-A85882E01B12> /Applications/Slicer_4.13_11_12.app/Contents/Frameworks/QtCore.framework/Versions/5/QtCore
           0x104e43000 -        0x104e77ffb +org.qt-project.QtXml (5.15 - 5.15.1) <FF0D928E-F0CF-3AE7-9251-20DC16FB3E00> /Applications/Slicer_4.13_11_12.app/Contents/Frameworks/QtXml.framework/Versions/5/QtXml
           0x104e89000 -        0x10518eff7 +org.qt-project.QtXmlPatterns (5.15 - 5.15.1) <867F0486-4D67-3845-83DC-55E8D6287E4E> /Applications/Slicer_4.13_11_12.app/Contents/Frameworks/QtXmlPatterns.framework/Versions/5/QtXmlPatterns
           0x1051dd000 -        0x105211ff7 +org.qt-project.QtSvg (5.15 - 5.15.1) <B681B523-D87F-3E19-933C-48A1FC8A30A2> /Applications/Slicer_4.13_11_12.app/Contents/Frameworks/QtSvg.framework/Versions/5/QtSvg
           0x10522f000 -        0x105262ff3 +org.qt-project.QtWebEngine (5.15 - 5.15.1) <603F3C84-2FF3-3586-A208-23A0D5E0637A> /Applications/Slicer_4.13_11_12.app/Contents/Frameworks/QtWebEngine.framework/Versions/5/QtWebEngine
           0x1052a0000 -        0x1052c4ff7 +org.qt-project.QtWebEngineWidgets (5.15 - 5.15.1) <9837CB4B-3D89-3BC9-B9E5-5DA84F19B6BB> /Applications/Slicer_4.13_11_12.app/Contents/Frameworks/QtWebEngineWidgets.framework/Versions/5/QtWebEngineWidgets
           0x1052f1000 -        0x10531cff7 +org.qt-project.QtPrintSupport (5.15 - 5.15.1) <0850C343-9B29-3F21-85D3-99788C3BC2CB> /Applications/Slicer_4.13_11_12.app/Contents/Frameworks/QtPrintSupport.framework/Versions/5/QtPrintSupport
           0x10533f000 -        0x10cbd5fe7 +org.qt-project.Qt.QtWebEngineCore (5.15 - 5.15.1) <3603AA86-387C-3ABC-BC4B-15DC9359227E> /Applications/Slicer_4.13_11_12.app/Contents/Frameworks/QtWebEngineCore.framework/Versions/5/QtWebEngineCore
           0x10d3f1000 -        0x10d457ff3 +org.qt-project.QtPositioning (5.15 - 5.15.1) <29534D27-0999-3383-B2A7-660F64B18F0D> /Applications/Slicer_4.13_11_12.app/Contents/Frameworks/QtPositioning.framework/Versions/5/QtPositioning
           0x10d477000 -        0x10d7bbffb +org.qt-project.QtQuick (5.15 - 5.15.1) <E694C21E-C7C1-32FF-B954-088264899A2A> /Applications/Slicer_4.13_11_12.app/Contents/Frameworks/QtQuick.framework/Versions/5/QtQuick
           0x10d8f2000 -        0x10d94aff3 +org.qt-project.QtQmlModels (5.15 - 5.15.1) <ED38C4D9-7F50-395A-B733-BE3A0F4B120D> /Applications/Slicer_4.13_11_12.app/Contents/Frameworks/QtQmlModels.framework/Versions/5/QtQmlModels
           0x10d96c000 -        0x10d985ff3 +org.qt-project.QtWebChannel (5.15 - 5.15.1) <DC898502-CD66-368C-96F6-3AC9A296D82F> /Applications/Slicer_4.13_11_12.app/Contents/Frameworks/QtWebChannel.framework/Versions/5/QtWebChannel
           0x10d994000 -        0x10dcedff7 +org.qt-project.QtQml (5.15 - 5.15.1) <14EF2A8D-DEA9-3A11-88D9-9A290C39FE3B> /Applications/Slicer_4.13_11_12.app/Contents/Frameworks/QtQml.framework/Versions/5/QtQml
           0x10dd9a000 -        0x10ddd5fff +org.qt-project.QtTest (5.15 - 5.15.1) <3094BC77-545F-36DA-9F14-4DDB2F3E00B5> /Applications/Slicer_4.13_11_12.app/Contents/Frameworks/QtTest.framework/Versions/5/QtTest
           0x10ddec000 -        0x10ddedff3 +_locale.so (0) <52C0E792-0A67-3ACB-82CF-0DEEE4E2FCEB> /Applications/Slicer_4.13_11_12.app/Contents/lib/Python/lib/python3.6/lib-dynload/_locale.so
           0x10ddf0000 -        0x10de56ff7 +libSlicerBaseLogic.dylib (0) <AB00FD62-71CD-3B43-954C-F940FFF7CD03> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libSlicerBaseLogic.dylib
           0x10de77000 -        0x10de78ff7 +_heapq.so (0) <90D43FD8-054D-3C19-B7DF-8C00F991BB42> /Applications/Slicer_4.13_11_12.app/Contents/lib/Python/lib/python3.6/lib-dynload/_heapq.so
           0x10de7c000 -        0x10dee6ff3 +libMRMLDisplayableManager.dylib (0) <27FB5FC1-65B7-3E63-AECF-886BFB5D196A> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libMRMLDisplayableManager.dylib
           0x10df2d000 -        0x10df8aff3 +libMRMLLogic.dylib (0) <048B12B9-9AB9-318B-9E6B-C0F85AFBA26B> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libMRMLLogic.dylib
           0x10dfae000 -        0x10dfafff7 +vtkCommonKitPython.so (0) <D7FBBC9C-1E20-3624-AF87-8CFF9045952F> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/vtkCommonKitPython.so
           0x10dfb3000 -        0x10e032ff7 +libRemoteIO.dylib (0) <F3427DFA-45CF-35C0-BF3C-064C6202AB8C> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libRemoteIO.dylib
           0x10e045000 -        0x10e047ff7 +vtkImagingKitPython.so (0) <8B7E7A92-00F7-3A7D-913A-990C8A4127CC> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/vtkImagingKitPython.so
           0x10e04a000 -        0x10e0a8ffb +libssl.1.1.dylib (0) <8AB4F07D-F1CA-365B-8943-2C66FD1E9BAB> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libssl.1.1.dylib
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           0x13713e000 -        0x1371c5ff3 +libqSlicerCropVolumeModule.dylib (0) <4300EBDC-E9F8-3E33-B73E-EF8BAF5C1C72> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/qt-loadable-modules/libqSlicerCropVolumeModule.dylib
           0x1371d4000 -        0x137223ffb +libvtkSlicerCropVolumeModuleLogic.dylib (0) <729E0CF9-5A50-362E-857C-725D59B06894> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/qt-loadable-modules/libvtkSlicerCropVolumeModuleLogic.dylib
           0x13722d000 -        0x13727dffb +libvtkSlicerVolumesModuleLogic.dylib (0) <A554CFDC-0B04-3F29-944A-3DA123357F2A> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/qt-loadable-modules/libvtkSlicerVolumesModuleLogic.dylib
           0x137289000 -        0x1372c8fff +libvtkSlicerCropVolumeModuleMRML.dylib (0) <9C37B3C1-0818-3280-94BE-9FA7047E564E> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/qt-loadable-modules/libvtkSlicerCropVolumeModuleMRML.dylib
           0x1372cf000 -        0x1372ebff3 +libITKDICOMParser-5.1.1.dylib (0) <32307980-DA54-3B06-BC69-83B3EF49D850> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libITKDICOMParser-5.1.1.dylib
           0x1372fb000 -        0x137316fff +libITKIOBruker-5.1.1.dylib (0) <6EE77772-3B92-3538-87C4-6E4B890349B8> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libITKIOBruker-5.1.1.dylib
           0x137328000 -        0x13734efff +libITKIOHDF5-5.1.1.dylib (0) <67BE4D1A-9182-3244-8B38-CF469891BBA9> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libITKIOHDF5-5.1.1.dylib
           0x137369000 -        0x13737cffb +libITKIOJPEG2000-5.1.1.dylib (0) <314783DB-9627-318B-9F59-F99BC461004B> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libITKIOJPEG2000-5.1.1.dylib
           0x137387000 -        0x1373a5fff +libITKOptimizersv4-5.1.1.dylib (0) <8C28F90B-9162-3A8E-BCFE-7B6886932976> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libITKOptimizersv4-5.1.1.dylib
           0x1373ba000 -        0x1373bffff +libitklbfgs-5.1.1.dylib (0) <19B7F7A1-847B-3559-B151-0C9774EE031E> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libitklbfgs-5.1.1.dylib
           0x1373c2000 -        0x1373dfff3 +libITKReview-5.1.1.dylib (0) <530B07F4-6337-3AFC-85C9-054AD2541293> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libITKReview-5.1.1.dylib
           0x1373ec000 -        0x1373edffb +libITKFastMarching-5.1.1.dylib (0) <BCD3F130-109D-3797-818E-3D86661FFAFB> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libITKFastMarching-5.1.1.dylib
           0x1373f0000 -        0x1373f6fff +libITKPolynomials-5.1.1.dylib (0) <F7690E77-D6A2-3E83-B998-20003D225B00> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libITKPolynomials-5.1.1.dylib
           0x1373fc000 -        0x137401ffb +libITKBiasCorrection-5.1.1.dylib (0) <935622BF-2109-350D-8945-D8A7D6530DA6> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libITKBiasCorrection-5.1.1.dylib
           0x137405000 -        0x137408ff3 +libITKFFT-5.1.1.dylib (0) <9D0ABA9F-AB67-3F25-89AD-82ACBEEB2227> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libITKFFT-5.1.1.dylib
           0x13740b000 -        0x137410ff7 +libITKDeformableMesh-5.1.1.dylib (0) <097997F2-B237-32EF-A31F-AAD424126A41> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libITKDeformableMesh-5.1.1.dylib
           0x137413000 -        0x137414ffb +libITKDiffusionTensorImage-5.1.1.dylib (0) <52F9553D-7778-3B16-907C-FDA1156532D7> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libITKDiffusionTensorImage-5.1.1.dylib
           0x137417000 -        0x13741cff3 +libITKPDEDeformableRegistration-5.1.1.dylib (0) <582B3DF4-E804-3CDD-B7D2-EF7671021AC9> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libITKPDEDeformableRegistration-5.1.1.dylib
           0x13741f000 -        0x137462ff3 +libITKOptimizers-5.1.1.dylib (0) <877452FD-FE9C-3B9F-8BD5-3E5FD6C7AEE2> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libITKOptimizers-5.1.1.dylib
           0x137483000 -        0x1374aaff7 +libitkopenjpeg-5.1.1.dylib (0) <17E50317-139F-30C0-A435-CA38F8E4944D> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libitkopenjpeg-5.1.1.dylib
           0x1374b2000 -        0x1374baff7 +libITKIOSiemens-5.1.1.dylib (0) <80A17CCB-2D99-3165-870E-4AB2E6096CEF> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libITKIOSiemens-5.1.1.dylib
           0x1374c3000 -        0x1374ceff3 +libITKKLMRegionGrowing-5.1.1.dylib (0) <100317F9-44D1-351E-81DA-8E4AB101ACEB> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libITKKLMRegionGrowing-5.1.1.dylib
           0x1374d6000 -        0x1374daffb +libITKMarkovRandomFieldsClassifiers-5.1.1.dylib (0) <F4AC4493-31AA-3314-BF70-FCE738174822> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libITKMarkovRandomFieldsClassifiers-5.1.1.dylib
           0x1374dd000 -        0x1374e1fff +libITKQuadEdgeMeshFiltering-5.1.1.dylib (0) <8280AB10-FC91-38AD-BA52-59BBDCCEF00A> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libITKQuadEdgeMeshFiltering-5.1.1.dylib
           0x1374e4000 -        0x1374eaffb +libITKWatersheds-5.1.1.dylib (0) <50C6A766-534B-3FDD-B47F-C2EEF4E20FAF> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libITKWatersheds-5.1.1.dylib
           0x1374ef000 -        0x137623ffb +libITKTestKernel-5.1.1.dylib (0) <A2A1C4F6-310D-30B4-958E-26FBF1A1D546> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libITKTestKernel-5.1.1.dylib
           0x1376b0000 -        0x1376c0fff +libITKIOMeshBYU-5.1.1.dylib (0) <DAEDC399-DE0D-3E0D-A67B-7682E86C33E3> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libITKIOMeshBYU-5.1.1.dylib
           0x1376cb000 -        0x1376e6ff3 +libITKIOMeshFreeSurfer-5.1.1.dylib (0) <4F0D30EE-4F40-3266-961C-F2B72FD17C9F> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libITKIOMeshFreeSurfer-5.1.1.dylib
           0x1376f4000 -        0x13770dff7 +libITKIOMeshGifti-5.1.1.dylib (0) <36039342-7B12-3BEA-988F-BED97E4582B7> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libITKIOMeshGifti-5.1.1.dylib
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           0x137745000 -        0x137759fff +libITKIOMeshOBJ-5.1.1.dylib (0) <DB72EC98-1857-3C9D-9913-8ECA656FD778> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libITKIOMeshOBJ-5.1.1.dylib
           0x137766000 -        0x13777cff7 +libITKIOMeshOFF-5.1.1.dylib (0) <AF5BE233-0BBC-3182-B36D-8CBBFE3DDF75> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libITKIOMeshOFF-5.1.1.dylib
           0x137789000 -        0x137838ff7 +libITKIOMeshVTK-5.1.1.dylib (0) <0EDE98AE-D574-3923-9646-B8E1291728F4> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libITKIOMeshVTK-5.1.1.dylib
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           0x13786a000 -        0x137877ff3 +libITKVideoIO-5.1.1.dylib (0) <1EAE5634-DCD3-321D-8226-5A099DF39AAD> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/libITKVideoIO-5.1.1.dylib
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           0x13796f000 -        0x1379f6ff7 +libvtkSlicerMarkupsModuleMRML.dylib (0) <C0459684-01F8-3B3C-A98D-723D402BB19D> /Applications/Slicer_4.13_11_12.app/Contents/lib/Slicer-4.13/qt-loadable-modules/libvtkSlicerMarkupsModuleMRML.dylib
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        0x7fff6bc11000 -     0x7fff6bc1dff9  libAudioStatistics.dylib (1104.93) <42CAC6A2-BCC3-391B-B1C1-D0AC69E99CF4> /usr/lib/libAudioStatistics.dylib
        0x7fff6bc1e000 -     0x7fff6bc51ffa  libAudioToolboxUtility.dylib (1104.93) <A7249C4C-6C0A-3C14-BA27-DC966F6CC6A0> /usr/lib/libAudioToolboxUtility.dylib
        0x7fff6bc58000 -     0x7fff6bc8cfff  libCRFSuite.dylib (48) <5E5DE3CB-30DD-34DC-AEF8-FE8536A85E96> /usr/lib/libCRFSuite.dylib
        0x7fff6bc8f000 -     0x7fff6bc99fff  libChineseTokenizer.dylib (34) <7F0DA183-1796-315A-B44A-2C234C7C50BE> /usr/lib/libChineseTokenizer.dylib
        0x7fff6bc9a000 -     0x7fff6bd22fff  libCoreStorage.dylib (551) <A457B0FE-D77F-30AA-99A4-70F6A98DFE59> /usr/lib/libCoreStorage.dylib
        0x7fff6bd25000 -     0x7fff6bd27ff7  libDiagnosticMessagesClient.dylib (112) <C94F3B7B-1854-38EB-9778-834501C53B3F> /usr/lib/libDiagnosticMessagesClient.dylib
        0x7fff6bd6d000 -     0x7fff6bf24ffb  libFosl_dynamic.dylib (100.4) <737573B2-190A-3BA1-8220-807AD0A2CE5E> /usr/lib/libFosl_dynamic.dylib
        0x7fff6bf4b000 -     0x7fff6bf51ff3  libIOReport.dylib (54) <75D177C4-BAD7-3285-B8E1-3019F49B3178> /usr/lib/libIOReport.dylib
        0x7fff6c033000 -     0x7fff6c03afff  libMatch.1.dylib (36) <5C6F3971-9D9E-3630-BDB6-60BFC5A665E0> /usr/lib/libMatch.1.dylib
        0x7fff6c069000 -     0x7fff6c089ff7  libMobileGestalt.dylib (826.140.5) <2BE94E6A-FA61-312F-84A1-F764D71B7E39> /usr/lib/libMobileGestalt.dylib
        0x7fff6c109000 -     0x7fff6c1e6ff7  libSMC.dylib (20) <5C9C17F2-1E6F-3A19-A440-86F74D82284F> /usr/lib/libSMC.dylib
        0x7fff6c1fb000 -     0x7fff6c1fcfff  libSystem.B.dylib (1281.100.1) <0A6C8BA1-30FD-3D10-83FD-FF29E221AFFE> /usr/lib/libSystem.B.dylib
        0x7fff6c1fd000 -     0x7fff6c288ff7  libTelephonyUtilDynamic.dylib (5017.1) <174030B2-E35F-3F17-A9EF-DF8631F30CCF> /usr/lib/libTelephonyUtilDynamic.dylib
        0x7fff6c289000 -     0x7fff6c28afff  libThaiTokenizer.dylib (3) <4F4ADE99-0D09-3223-B7C0-C407AB6DE8DC> /usr/lib/libThaiTokenizer.dylib
        0x7fff6c2a2000 -     0x7fff6c2b8fff  libapple_nghttp2.dylib (1.39.2) <07FEC48A-87CF-32A3-8194-FA70B361713A> /usr/lib/libapple_nghttp2.dylib
        0x7fff6c2ed000 -     0x7fff6c35fff7  libarchive.2.dylib (72.140.1) <AC311FBA-F2DD-3595-AA76-769F912942B8> /usr/lib/libarchive.2.dylib
        0x7fff6c360000 -     0x7fff6c3f9fe5  libate.dylib (3.0.1) <76EA60FB-748C-313F-8951-B076540BEA97> /usr/lib/libate.dylib
        0x7fff6c3fd000 -     0x7fff6c3fdff3  libauto.dylib (187) <B6124448-7690-34AE-8939-ED84AAC630CE> /usr/lib/libauto.dylib
        0x7fff6c4c3000 -     0x7fff6c4d3ffb  libbsm.0.dylib (60.100.1) <00BFFB9A-2FFE-3C24-896A-251BC61917FD> /usr/lib/libbsm.0.dylib
        0x7fff6c4d4000 -     0x7fff6c4e0fff  libbz2.1.0.dylib (44) <14CC4988-B6D4-3879-AFC2-9A0DDC6388DE> /usr/lib/libbz2.1.0.dylib
        0x7fff6c4e1000 -     0x7fff6c533fff  libc++.1.dylib (902.1) <59A8239F-C28A-3B59-B8FA-11340DC85EDC> /usr/lib/libc++.1.dylib
        0x7fff6c534000 -     0x7fff6c549ffb  libc++abi.dylib (902) <E692F14F-C65E-303B-9921-BB7E97D77855> /usr/lib/libc++abi.dylib
        0x7fff6c54a000 -     0x7fff6c54afff  libcharset.1.dylib (59) <72447768-9244-39AB-8E79-2FA14EC0AD33> /usr/lib/libcharset.1.dylib
        0x7fff6c54b000 -     0x7fff6c55cfff  libcmph.dylib (8) <E72A20DB-2E86-378D-A237-EB9A1370F989> /usr/lib/libcmph.dylib
        0x7fff6c55d000 -     0x7fff6c574fd7  libcompression.dylib (87) <64C91066-586D-38C0-A2F3-3E60A940F859> /usr/lib/libcompression.dylib
        0x7fff6c84e000 -     0x7fff6c864ff7  libcoretls.dylib (167) <770A5B96-936E-34E3-B006-B1CEC299B5A5> /usr/lib/libcoretls.dylib
        0x7fff6c865000 -     0x7fff6c866fff  libcoretls_cfhelpers.dylib (167) <940BF370-FD0C-30A8-AA05-FF48DA44FA4C> /usr/lib/libcoretls_cfhelpers.dylib
        0x7fff6ce10000 -     0x7fff6ce1bfff  libcsfde.dylib (551) <CEE22AEF-4E77-36B1-B007-00303819965F> /usr/lib/libcsfde.dylib
        0x7fff6ce23000 -     0x7fff6ce82ff7  libcups.2.dylib (483.6) <C88D78FE-D238-376C-B16A-39270E39A79D> /usr/lib/libcups.2.dylib
        0x7fff6cf8c000 -     0x7fff6cf8cfff  libenergytrace.dylib (21) <162DFCC0-8F48-3DD0-914F-FA8653E27B26> /usr/lib/libenergytrace.dylib
        0x7fff6cf8d000 -     0x7fff6cfa5fff  libexpat.1.dylib (19.60.2) <FED7C38B-92D8-342D-AED7-871B12D1F7E7> /usr/lib/libexpat.1.dylib
        0x7fff6cfb3000 -     0x7fff6cfb5fff  libfakelink.dylib (149.1) <36146CB2-E6A5-37BB-9EE8-1B4034D8F3AD> /usr/lib/libfakelink.dylib
        0x7fff6cfc4000 -     0x7fff6cfc9fff  libgermantok.dylib (24) <D2AE5AC0-EDCE-3216-B8C9-CF59292A545F> /usr/lib/libgermantok.dylib
        0x7fff6cfca000 -     0x7fff6cfd3ff7  libheimdal-asn1.dylib (564.140.1) <0AC6FB62-2B0F-3E93-A931-E4DC4B1D757A> /usr/lib/libheimdal-asn1.dylib
        0x7fff6cfd4000 -     0x7fff6d0c4fff  libiconv.2.dylib (59) <18311A67-E4EF-3CC7-95B3-C0EDEE3A282F> /usr/lib/libiconv.2.dylib
        0x7fff6d0c5000 -     0x7fff6d31cfff  libicucore.A.dylib (64260.0.1) <8AC2CB07-E7E0-340D-A849-186FA1F27251> /usr/lib/libicucore.A.dylib
        0x7fff6d336000 -     0x7fff6d337fff  liblangid.dylib (133) <30CFC08C-EF36-3CF5-8AEA-C1CB070306B7> /usr/lib/liblangid.dylib
        0x7fff6d338000 -     0x7fff6d350ff3  liblzma.5.dylib (16) <C131EF18-2CDD-3271-8A30-A8760D4FE166> /usr/lib/liblzma.5.dylib
        0x7fff6d368000 -     0x7fff6d40fff7  libmecab.dylib (883.11) <0D5BFD01-D4A7-3C8D-AA69-C329C1A69792> /usr/lib/libmecab.dylib
        0x7fff6d410000 -     0x7fff6d672ff1  libmecabra.dylib (883.11) <E31DE74D-1B88-377F-ACD3-D789D29C3AE7> /usr/lib/libmecabra.dylib
        0x7fff6d9df000 -     0x7fff6da0efff  libncurses.5.4.dylib (57) <995DFEEA-40F3-377F-B73D-D02AC59D591F> /usr/lib/libncurses.5.4.dylib
        0x7fff6db3e000 -     0x7fff6dfbaff5  libnetwork.dylib (1880.120.4) <BA294A54-F309-398D-B308-F97032AFF555> /usr/lib/libnetwork.dylib
        0x7fff6dfbb000 -     0x7fff6dfd2fff  libnetworkextension.dylib (1095.140.2) <D0E8454C-33A9-3F96-B3A0-EDB12C32283A> /usr/lib/libnetworkextension.dylib
        0x7fff6e05b000 -     0x7fff6e08efde  libobjc.A.dylib (787.1) <6DF81160-5E7F-3E31-AA1E-C875E3B98AF6> /usr/lib/libobjc.A.dylib
        0x7fff6e0a1000 -     0x7fff6e0a5fff  libpam.2.dylib (25.100.1) <0502F395-8EE6-3D2A-9239-06FD5622E19E> /usr/lib/libpam.2.dylib
        0x7fff6e0a8000 -     0x7fff6e0deff7  libpcap.A.dylib (89.120.1) <A76EC076-A8EA-354C-B95F-7AB1EAFBCC65> /usr/lib/libpcap.A.dylib
        0x7fff6e11e000 -     0x7fff6e12cff9  libperfcheck.dylib (37.100.2) <9D9C4879-8A80-34C4-B0D2-BE341FD6D321> /usr/lib/libperfcheck.dylib
        0x7fff6e12d000 -     0x7fff6e130ff3  libpmenergy.dylib (214.120.1) <B33FB1C8-EACB-39C5-9B71-283EAB72E7E2> /usr/lib/libpmenergy.dylib
        0x7fff6e131000 -     0x7fff6e133fff  libpmsample.dylib (214.120.1) <F12D8EE0-9284-3E57-AE73-414266E11586> /usr/lib/libpmsample.dylib
        0x7fff6e162000 -     0x7fff6e17afff  libresolv.9.dylib (67.40.1) <C57EDFEF-D36A-310B-8D14-8C68A625B1E8> /usr/lib/libresolv.9.dylib
        0x7fff6e17c000 -     0x7fff6e1c0ff7  libsandbox.1.dylib (1217.141.2) <E8BA5E84-66AF-3995-8F8E-DDC93B0A88E1> /usr/lib/libsandbox.1.dylib
        0x7fff6e1d4000 -     0x7fff6e1d5ff7  libspindump.dylib (281.3) <AE8C1AE9-5CBC-332F-BBE8-370A2A19FED6> /usr/lib/libspindump.dylib
        0x7fff6e1d6000 -     0x7fff6e3c0ff7  libsqlite3.dylib (308.5) <35A2BD9F-4E33-30DE-A994-4AB585AC3AFE> /usr/lib/libsqlite3.dylib
        0x7fff6e4e4000 -     0x7fff6e52eff7  libstdc++.6.dylib (104.1) <1AD78D98-D287-3A49-AFBB-4999CB334D2B> /usr/lib/libstdc++.6.dylib
        0x7fff6e5b6000 -     0x7fff6e610ff8  libusrtcp.dylib (1880.120.4) <05346A91-737C-33D0-B21B-F040950DFB28> /usr/lib/libusrtcp.dylib
        0x7fff6e611000 -     0x7fff6e614ffb  libutil.dylib (57) <F01467F6-23A7-37EE-A170-33CE1577B41D> /usr/lib/libutil.dylib
        0x7fff6e615000 -     0x7fff6e622ff7  libxar.1.dylib (425.2) <EE964412-9E25-30B3-BCC0-CCEFBCC8094B> /usr/lib/libxar.1.dylib
        0x7fff6e628000 -     0x7fff6e70afff  libxml2.2.dylib (33.5) <A579D158-2E09-316C-872E-DD9D93401B2F> /usr/lib/libxml2.2.dylib
        0x7fff6e70e000 -     0x7fff6e736fff  libxslt.1.dylib (16.9) <34A45627-DA5B-37D2-9609-65B425E0010A> /usr/lib/libxslt.1.dylib
        0x7fff6e737000 -     0x7fff6e749ff3  libz.1.dylib (76) <793D9643-CD83-3AAC-8B96-88D548FAB620> /usr/lib/libz.1.dylib
        0x7fff6eff8000 -     0x7fff6effdff3  libcache.dylib (83) <AF488D13-9E89-35E0-B078-BE37CC5B8586> /usr/lib/system/libcache.dylib
        0x7fff6effe000 -     0x7fff6f009fff  libcommonCrypto.dylib (60165.120.1) <C7912BE5-993E-3581-B2A0-6AABDC8C5562> /usr/lib/system/libcommonCrypto.dylib
        0x7fff6f00a000 -     0x7fff6f011fff  libcompiler_rt.dylib (101.2) <49B8F644-5705-3F16-BBE0-6FFF9B17C36E> /usr/lib/system/libcompiler_rt.dylib
        0x7fff6f012000 -     0x7fff6f01bff7  libcopyfile.dylib (166.40.1) <3C481225-21E7-370A-A30E-0CCFDD64A92C> /usr/lib/system/libcopyfile.dylib
        0x7fff6f01c000 -     0x7fff6f0aefdb  libcorecrypto.dylib (866.140.1) <60567BF8-80FA-359A-B2F3-A3BAEFB288FD> /usr/lib/system/libcorecrypto.dylib
        0x7fff6f1bb000 -     0x7fff6f1fbff0  libdispatch.dylib (1173.100.2) <CD9C059C-91D9-30E8-8926-5B9CD0D5D4F5> /usr/lib/system/libdispatch.dylib
        0x7fff6f1fc000 -     0x7fff6f232fff  libdyld.dylib (750.6) <789A18C2-8AC7-3C88-813D-CD674376585D> /usr/lib/system/libdyld.dylib
        0x7fff6f233000 -     0x7fff6f233ffb  libkeymgr.dylib (30) <DB3337BE-01CA-3425-BD0C-87774FC0CDC0> /usr/lib/system/libkeymgr.dylib
        0x7fff6f234000 -     0x7fff6f240ff3  libkxld.dylib (6153.141.2) <EE8ECB4B-2EDB-3440-BBC1-6BDDDF5F1BCE> /usr/lib/system/libkxld.dylib
        0x7fff6f241000 -     0x7fff6f241ff7  liblaunch.dylib (1738.140.1) <AFBCBDD3-0B55-3ECD-8E04-A73A3A57356B> /usr/lib/system/liblaunch.dylib
        0x7fff6f242000 -     0x7fff6f247ff7  libmacho.dylib (959.0.1) <AA613A9C-961A-3B67-B696-4622FA59FC4E> /usr/lib/system/libmacho.dylib
        0x7fff6f248000 -     0x7fff6f24aff3  libquarantine.dylib (110.40.3) <F234E51D-FD0B-3EE4-B679-AE3EE9C536C3> /usr/lib/system/libquarantine.dylib
        0x7fff6f24b000 -     0x7fff6f24cff7  libremovefile.dylib (48) <7C7EFC79-BD24-33EF-B073-06AED234593E> /usr/lib/system/libremovefile.dylib
        0x7fff6f24d000 -     0x7fff6f264ff3  libsystem_asl.dylib (377.60.2) <1563EE02-0657-3B78-99BE-A947C24122EF> /usr/lib/system/libsystem_asl.dylib
        0x7fff6f265000 -     0x7fff6f265ff7  libsystem_blocks.dylib (74) <0D53847E-AF5F-3ACF-B51F-A15DEA4DEC58> /usr/lib/system/libsystem_blocks.dylib
        0x7fff6f266000 -     0x7fff6f2edfff  libsystem_c.dylib (1353.100.2) <BBDED5E6-A646-3EED-B33A-91E4331EA063> /usr/lib/system/libsystem_c.dylib
        0x7fff6f2ee000 -     0x7fff6f2f1ffb  libsystem_configuration.dylib (1061.141.1) <0EE84C33-64FD-372B-974A-AF7A136F2068> /usr/lib/system/libsystem_configuration.dylib
        0x7fff6f2f2000 -     0x7fff6f2f5fff  libsystem_coreservices.dylib (114) <A199156E-058D-3ABB-BCE9-4B9F20DCED0F> /usr/lib/system/libsystem_coreservices.dylib
        0x7fff6f2f6000 -     0x7fff6f2fefff  libsystem_darwin.dylib (1353.100.2) <5B12B5DB-3F30-37C1-8ECC-49A66B1F2864> /usr/lib/system/libsystem_darwin.dylib
        0x7fff6f2ff000 -     0x7fff6f306fff  libsystem_dnssd.dylib (1096.100.3) <EBB4C2C2-E031-3094-B40A-E67BF261D295> /usr/lib/system/libsystem_dnssd.dylib
        0x7fff6f307000 -     0x7fff6f308ffb  libsystem_featureflags.dylib (17) <29FD922A-EC2C-3F25-BCCC-B58D716E60EC> /usr/lib/system/libsystem_featureflags.dylib
        0x7fff6f309000 -     0x7fff6f356ff7  libsystem_info.dylib (538) <8A321605-5480-330B-AF9E-64E65DE61747> /usr/lib/system/libsystem_info.dylib
        0x7fff6f357000 -     0x7fff6f383ff7  libsystem_kernel.dylib (6153.141.2) <A576A1CF-7726-3146-B04B-A26E1CDB9757> /usr/lib/system/libsystem_kernel.dylib
        0x7fff6f384000 -     0x7fff6f3cbfff  libsystem_m.dylib (3178) <00F331F1-0D09-39B3-8736-1FE90E64E903> /usr/lib/system/libsystem_m.dylib
        0x7fff6f3cc000 -     0x7fff6f3f3fff  libsystem_malloc.dylib (283.100.6) <8549294E-4C53-36EB-99F3-584A7393D8D5> /usr/lib/system/libsystem_malloc.dylib
        0x7fff6f3f4000 -     0x7fff6f401ffb  libsystem_networkextension.dylib (1095.140.2) <F06C65C5-2CBE-313C-96E1-A09240F9FE57> /usr/lib/system/libsystem_networkextension.dylib
        0x7fff6f402000 -     0x7fff6f40bff7  libsystem_notify.dylib (241.100.2) <FA22F928-D91B-3AA5-96BB-3186AC0FB264> /usr/lib/system/libsystem_notify.dylib
        0x7fff6f40c000 -     0x7fff6f414fef  libsystem_platform.dylib (220.100.1) <009A7C1F-313A-318E-B9F2-30F4C06FEA5C> /usr/lib/system/libsystem_platform.dylib
        0x7fff6f415000 -     0x7fff6f41ffff  libsystem_pthread.dylib (416.100.3) <62CB1A98-0B8F-31E7-A02B-A1139927F61D> /usr/lib/system/libsystem_pthread.dylib
        0x7fff6f420000 -     0x7fff6f424ff3  libsystem_sandbox.dylib (1217.141.2) <051C4018-4345-3034-AC98-6DE42FB8273B> /usr/lib/system/libsystem_sandbox.dylib
        0x7fff6f425000 -     0x7fff6f427fff  libsystem_secinit.dylib (62.100.2) <F80872AA-E1FD-3D7E-8729-467656EC6561> /usr/lib/system/libsystem_secinit.dylib
        0x7fff6f428000 -     0x7fff6f42fffb  libsystem_symptoms.dylib (1238.120.1) <5820A2AF-CE72-3AB3-ABCC-273A3419FB55> /usr/lib/system/libsystem_symptoms.dylib
        0x7fff6f430000 -     0x7fff6f446ff2  libsystem_trace.dylib (1147.120) <04B47629-847B-3D74-8ABE-C05EF9DEEFE4> /usr/lib/system/libsystem_trace.dylib
        0x7fff6f448000 -     0x7fff6f44dff7  libunwind.dylib (35.4) <42B7B509-BAFE-365B-893A-72414C92F5BF> /usr/lib/system/libunwind.dylib
        0x7fff6f44e000 -     0x7fff6f483ffe  libxpc.dylib (1738.140.1) <3E243A41-030F-38E3-9FD2-7B38C66C35B1> /usr/lib/system/libxpc.dylib
    
    External Modification Summary:
      Calls made by other processes targeting this process:
        task_for_pid: 2
        thread_create: 0
        thread_set_state: 0
      Calls made by this process:
        task_for_pid: 0
        thread_create: 0
        thread_set_state: 0
      Calls made by all processes on this machine:
        task_for_pid: 38340012
        thread_create: 0
        thread_set_state: 0
    
    VM Region Summary:
    ReadOnly portion of Libraries: Total=1.4G resident=0K(0%) swapped_out_or_unallocated=1.4G(100%)
    Writable regions: Total=4.2G written=0K(0%) resident=0K(0%) swapped_out=0K(0%) unallocated=4.2G(100%)
     
                                      VIRTUAL   REGION 
    REGION TYPE                          SIZE    COUNT (non-coalesced) 
    ===========                       =======  ======= 
    Accelerate framework                 384K        3 
    Activity Tracing                     256K        1 
    CG backing stores                   2072K        4 
    CoreGraphics                           8K        1 
    CoreImage                             32K        4 
    Foundation                             4K        1 
    JS JIT generated code              128.0M        1 
    JS JIT generated code (reserved)     1.9G        1         reserved VM address space (unallocated)
    JS VM Gigacage                       256K        1 
    JS VM Isolated Heap (reserved)      4160K        1         reserved VM address space (unallocated)
    Kernel Alloc Once                      8K        1 
    MALLOC                             379.3M       71 
    MALLOC guard page                     48K        9 
    MALLOC_MEDIUM (reserved)             1.2G       10         reserved VM address space (unallocated)
    MALLOC_NANO (reserved)             384.0M        1         reserved VM address space (unallocated)
    STACK GUARD                         56.2M       43 
    Stack                               29.3M       43 
    VM_ALLOCATE                        129.4M      155 
    VM_ALLOCATE (reserved)             160.0M        3         reserved VM address space (unallocated)
    WebKit Malloc                       1184K        3 
    __DATA                              97.2M     1064 
    __DATA_CONST                          80K        2 
    __FONT_DATA                            4K        1 
    __GLSLBUILTINS                      5176K        1 
    __LINKEDIT                         478.0M      615 
    __OBJC_RO                           32.3M        1 
    __OBJC_RW                           1908K        2 
    __TEXT                             964.2M      982 
    __UNICODE                            564K        1 
    mapped file                        373.6M       30 
    shared memory                        656K       19 
    ===========                       =======  ======= 
    TOTAL                                6.2G     3075 
    TOTAL, minus reserved VM space       2.6G     3075 
    
    Model: MacBookPro16,1, BootROM 1037.147.4.0.0 (iBridge: 17.16.16610.0.0,0), 8 processors, 8-Core Intel Core i9, 2.4 GHz, 64 GB, SMC 
    Graphics: kHW_IntelUHDGraphics630Item, Intel UHD Graphics 630, spdisplays_builtin
    Graphics: kHW_AMDRadeonPro5500MItem, AMD Radeon Pro 5500M, spdisplays_pcie_device, 8 GB
    Memory Module: BANK 0/ChannelA-DIMM0, 32 GB, DDR4, 2667 MHz, Micron, -
    Memory Module: BANK 2/ChannelB-DIMM0, 32 GB, DDR4, 2667 MHz, Micron, -
    AirPort: spairport_wireless_card_type_airport_extreme (0x14E4, 0x7BF), wl0: Apr  7 2020 13:09:12 version 9.30.357.41.32.5.49 FWID 01-617711e7
    Bluetooth: Version 7.0.6f7, 3 services, 27 devices, 1 incoming serial ports
    Network Service: Wi-Fi, AirPort, en0
    USB Device: USB 3.1 Bus
    USB Device: USB2.0 Hub
    USB Device: Apple T2 Bus
    USB Device: Composite Device
    USB Device: Touch Bar Backlight
    USB Device: Touch Bar Display
    USB Device: Apple Internal Keyboard / Trackpad
    USB Device: Headset
    USB Device: Ambient Light Sensor
    USB Device: FaceTime HD Camera (Built-in)
    USB Device: Apple T2 Controller
    Thunderbolt Bus: MacBook Pro, Apple Inc., 55.3
    Thunderbolt Bus: MacBook Pro, Apple Inc., 55.3
    

    Expected behavior

    After installation, it should work and not cause 3D Slicer to crash

    Actual behavior

    After shutting down Slicer and trying to restart it, it crashes right away not even opening the main window.

    bug 
    opened by che85 25
  • Is there a convenient way to plot a slice from a patient volume?

    Is there a convenient way to plot a slice from a patient volume?

    Is there any convenient way to display for example nth-slice of subject image of subjects_dataset[0] ?

    I want to visualize slices as I apply tranforms to them, the only thing I came up with is converting the the 3D Tensor back to .nii image and from that plotting the slice.

    Can you suggest any convenient method ?

    Thankyou and much appreciated.

    Faraz

    opened by farazahmeds 25
  • 118 resample to target spacing

    118 resample to target spacing

    In the resampling transform, I have added the possibility to resample all images to a common voxelgrid as discussed in issue #118

    It's the first time I have been working directly on a package, so if you have any comments on the code I'm glad to get some feedback ;)

    opened by nwschurink 25
  • Added support for HDF5 Dataset as Image including lazy loading of patches

    Added support for HDF5 Dataset as Image including lazy loading of patches

    Will be able to use HDF5 datasets instead of path or tensor for creation of Image. The dataset can either be in (CxHxWxD) or even (HxWxD) format. If its the later, then the data will be expended for channel dim. With the lazypatch parameter, will be able to enable or disable lazy loading of patches (only the patched portion of the volume will be loaded in the memory from the HDF5 Dataset).

    • [x] I have read the CONTRIBUTING docs and have a developer setup (especially important are pre-commitand pytest)
    • [x] Non-breaking change (would not break existing functionality)
    • [ ] Breaking change (would cause existing functionality to change)
    • [x] Tests added or modified to cover the changes
    • [x] Integration tests passed locally by running pytest
    • [x] In-line docstrings updated
    • [ ] Documentation updated, tested running make html inside the docs/ folder
    • [x] This pull request is ready to be reviewed
    • [ ] If the PR is ready and there are multiple commits, I have squashed them and force-pushed
    opened by soumickmj 24
  • Adding CropOrPad

    Adding CropOrPad

    Hello,

    This time, I am making a cleaner PR for my CropOrPad implementation allowing to either use the center of the volume or the center of a given mask. This comes from the issue : #104

    I hope this helps.

    opened by GReguig 23
  • Fix copying of subclasses of Subject

    Fix copying of subclasses of Subject

    Fixes #694 .

    Description Adds support (and tests) for copying subclasses.

    Tests seem to be cleaner than #718 . Approach is almost the same!

    Checklist

    • [x] I have read the CONTRIBUTING docs and have a developer setup (especially important are pre-commitand pytest)
    • [x] Non-breaking change (would not break existing functionality)
    • [ ] Breaking change (would cause existing functionality to change)
    • [x] Tests added or modified to cover the changes
    • [ ] Integration tests passed locally by running pytest
    • [ ] In-line docstrings updated
    • [ ] Documentation updated, tested running make html inside the docs/ folder
    • [x] This pull request is ready to be reviewed
    • [x] If the PR is ready and there are multiple commits, I have squashed them and force-pushed
    opened by justusschock 22
  • Error when trying to GET torchio.Subject from torchio.SubjectDataset with multiple images with different spatial size

    Error when trying to GET torchio.Subject from torchio.SubjectDataset with multiple images with different spatial size

    I am trying to construct torchio.SubjectDataset where each torchio.Subject has multiple images (different sequences). Potential problem is, all these images are DIFFERENT spatial sizes.

    In order to bring them to same spatial size I've included CropOrPadtransformation. But, when I try to GET one subject from the dataset, I am getting following error:

    ---------------------------------------------------------------------------
    RuntimeError                              Traceback (most recent call last)
    <ipython-input-79-1b7789238bcb> in <module>
    ----> 1 subj = dataset.train_set[0]
          2 #help(tio.Subject)
          3 subj.__dict__
    
    /opt/conda/lib/python3.7/site-packages/torchio/data/dataset.py in __getitem__(self, index)
         83         # Apply transform (this is usually the bottleneck)
         84         if self._transform is not None:
    ---> 85             subject = self._transform(subject)
         86         return subject
         87 
    
    /opt/conda/lib/python3.7/site-packages/torchio/transforms/transform.py in __call__(self, data)
        124             subject = copy.copy(subject)
        125         with np.errstate(all='raise', under='ignore'):
    --> 126             transformed = self.apply_transform(subject)
        127         if self.keep is not None:
        128             for name, image in images_to_keep.items():
    
    /opt/conda/lib/python3.7/site-packages/torchio/transforms/augmentation/composition.py in apply_transform(self, subject)
         45     def apply_transform(self, subject: Subject) -> Subject:
         46         for transform in self.transforms:
    ---> 47             subject = transform(subject)
         48         return subject
         49 
    
    /opt/conda/lib/python3.7/site-packages/torchio/transforms/transform.py in __call__(self, data)
        124             subject = copy.copy(subject)
        125         with np.errstate(all='raise', under='ignore'):
    --> 126             transformed = self.apply_transform(subject)
        127         if self.keep is not None:
        128             for name, image in images_to_keep.items():
    
    /opt/conda/lib/python3.7/site-packages/torchio/transforms/augmentation/composition.py in apply_transform(self, subject)
         45     def apply_transform(self, subject: Subject) -> Subject:
         46         for transform in self.transforms:
    ---> 47             subject = transform(subject)
         48         return subject
         49 
    
    /opt/conda/lib/python3.7/site-packages/torchio/transforms/transform.py in __call__(self, data)
        124             subject = copy.copy(subject)
        125         with np.errstate(all='raise', under='ignore'):
    --> 126             transformed = self.apply_transform(subject)
        127         if self.keep is not None:
        128             for name, image in images_to_keep.items():
    
    /opt/conda/lib/python3.7/site-packages/torchio/transforms/preprocessing/spatial/crop_or_pad.py in apply_transform(self, subject)
        238 
        239     def apply_transform(self, subject: Subject) -> Subject:
    --> 240         padding_params, cropping_params = self.compute_crop_or_pad(subject)
        241         padding_kwargs = {'padding_mode': self.padding_mode}
        242         if padding_params is not None:
    
    /opt/conda/lib/python3.7/site-packages/torchio/transforms/preprocessing/spatial/crop_or_pad.py in _compute_center_crop_or_pad(self, subject)
        157             subject: Subject,
        158             ) -> Tuple[Optional[TypeSixBounds], Optional[TypeSixBounds]]:
    --> 159         source_shape = subject.spatial_shape
        160         # The parent class turns the 3-element shape tuple (w, h, d)
        161         # into a 6-element bounds tuple (w, w, h, h, d, d)
    
    /opt/conda/lib/python3.7/site-packages/torchio/data/subject.py in spatial_shape(self)
        116             (181, 217, 181)
        117         """
    --> 118         self.check_consistent_spatial_shape()
        119         return self.get_first_image().spatial_shape
        120 
    
    /opt/conda/lib/python3.7/site-packages/torchio/data/subject.py in check_consistent_spatial_shape(self)
        294 
        295     def check_consistent_spatial_shape(self) -> None:
    --> 296         self.check_consistent_attribute('spatial_shape')
        297 
        298     def check_consistent_orientation(self) -> None:
    
    /opt/conda/lib/python3.7/site-packages/torchio/data/subject.py in check_consistent_attribute(self, attribute, relative_tolerance, absolute_tolerance, message)
        282                         }),
        283                     )
    --> 284                     raise RuntimeError(message)
        285         except TypeError:
        286             # fallback for non-numeric values
    
    RuntimeError: More than one value for "spatial_shape" found in subject images:
    {'T1w': (416, 512, 36), 'T2w': (448, 512, 36)}
    

    In summary, when it tries to apply CropOrPad transformation, it fails on following code block:

    /opt/conda/lib/python3.7/site-packages/torchio/data/subject.py in check_consistent_spatial_shape(self)
        294 
        295     def check_consistent_spatial_shape(self) -> None:
    --> 296         self.check_consistent_attribute('spatial_shape')
        297 
        298     def check_consistent_orientation(self) -> None:
    

    How do I turn off check_consistent_attribute('spatial_shape')? Because in the end all subjects WILL BE same size. But this breaks things before CropOrPadis applied.

    NOTE: Dataset is created SUCCESSFULLY, it is only when I try to GET one of its Subjects, and when transformations are being applied that code fails.

    opened by cepa995 22
  • Add pythonic slicing support to torchio.Image

    Add pythonic slicing support to torchio.Image

    🚀 Feature

    Allow torchio.Image to be sliced like numpy arrays

    image = torchio.Image(...)
    print(image.spatial_shape) # -> (100, 100, 100)
    
    crop = image[10:20, :, ::2] # returns a new ScalarImage where the affine matrix and data have been modified accordingly
    print(crop.spatial_shape) # -> (10, 100, 50)
    
    # image['data'] or image['affine'] would still work
    

    Motivation

    More pythonic cropping.

    Easier subsampling and flipping.

    Alternatives

    An alternative would be to use torchio.transforms.Crop but it does not support subsampling at regular intervals and flipping (eg. ::2 or ::-1).

    Additional context

    The __getitem__ method of torchio.Image could look like this:

    def __getitem__(self, item):
        if isinstance(item, str):
            if item in (DATA, AFFINE):
                if item not in self:
                    self.load()
            return super().__getitem__(item)
        else:
            check_validity(item) # do some checks
            if not self._loaded:
                 self.load()
            if not isinstance(item, tuple):
                item = (item,)
            new_data = self.data[(slice(None), *item)] # add the channel dim
            new_affine = modify_affine(self.affine, item) # I have a working implementation for this
            new_image = type(self)(tensor=new_data, affine=new_affine)
            return new_image
    
    enhancement 
    opened by marius-sm 0
  • Add support for Python 3.11

    Add support for Python 3.11

    Description Adding support for Python 3.11.

    Checklist

    • [x] I have read the CONTRIBUTING docs and have a developer setup (especially important are pre-commitand pytest)
    • [x] Non-breaking change (would not break existing functionality)
    • [ ] Breaking change (would cause existing functionality to change)
    • [ ] Tests added or modified to cover the changes
    • [ ] Integration tests passed locally by running pytest
    • [ ] In-line docstrings updated
    • [ ] Documentation updated, tested running make html inside the docs/ folder
    • [ ] This pull request is ready to be reviewed
    • [ ] If the PR is ready and there are multiple commits, I have squashed them and force-pushed
    opened by fepegar 1
  • Aggregator smaller model outputs  - Ready to be reviewed

    Aggregator smaller model outputs - Ready to be reviewed

    This is to reference #{1001}.

    I have described the issue and planned fix in detail I believe, this is a draft PR to ask for some help

    I have made most changes I believe, but this is a broken draft to show you my idea, let me know if the assertions are insufficient or if argument/attribute names are not clear

    The bit I'm confused with is the subject padding, the subject is now forced to be padded and the padding is set to be at least the difference between input and output. and the final aggregator output tensor isn't padded

    So say I have an input tensor with shape (128.128.128) , with a patch_size of (64,64,64) and a model output size of (60,60,60)

    The patch_difference is set to be (4,4,4) and overlap is made to be at least equal to this, when the subject is padded in the GridSampler does that mean it's shape is now (136,136,136)?

    Description https://github.com/fepegar/torchio/issues/1001

    Checklist

    • [x] I have read the CONTRIBUTING docs and have a developer setup (especially important are pre-commitand pytest)
    • [x] Non-breaking change (would not break existing functionality)
    • [ ] Breaking change (would cause existing functionality to change)
    • [x] Tests added or modified to cover the changes
    • [x] Integration tests passed locally by running pytest
    • [x] In-line docstrings updated
    • [x] Documentation updated, tested running make html inside the docs/ folder
    • [x] This pull request is ready to be reviewed
    • [x] If the PR is ready and there are multiple commits, I have squashed them and force-pushed
    opened by wahabk 3
  • GridAggregator does not support smaller output than input

    GridAggregator does not support smaller output than input

    Is there an existing issue for this?

    • [X] I have searched the existing issues

    Bug summary

    tio.inference.GridSampler and GridAggregator do not allow the model output to be smaller than the input.

    I was going to submit this as a feature request before making a PR, however, I realised that tio actually supports this depending on patch_overlap and overlap_mode so I believe this should be a bug.

    Code for reproduction

    # This is not a MWE but a test named `test_inference_smaller.py`
    
    from torch.utils.data import DataLoader
    from torchio import DATA
    from torchio import LOCATION
    from torchio.data.inference import GridAggregator
    from torchio.data.inference import GridSampler
    
    from ...utils import TorchioTestCase
    
    
    class TestInference(TorchioTestCase):
        """Tests for `inference` module."""
        def test_inference_no_padding(self):
            self.try_inference(None)
    
        def test_inference_padding(self):
            self.try_inference(3)
    
        def try_inference(self, padding_mode):
            for mode in ["crop", "average", "hann"]:
                for n in 17, 27:
                    patch_size = 10, 15, n
                    patch_overlap = 0, 0, 0 # <------------- this is important and different from the usual test
                    batch_size = 6
    
                    grid_sampler = GridSampler(
                        self.sample_subject,
                        patch_size,
                        patch_overlap,
                        padding_mode=padding_mode,
                    )
                    aggregator = GridAggregator(grid_sampler, overlap_mode=mode)
                    patch_loader = DataLoader(grid_sampler, batch_size=batch_size)
                    for patches_batch in patch_loader:
                        input_tensor = patches_batch['t1'][DATA]
                        locations = patches_batch[LOCATION]
                        logits = model(input_tensor)  # some model
                        outputs = logits
                        # 
                        i_ini, j_ini, k_ini = 1, 1, 1
                        i_fin, j_fin, k_fin = patch_size[0]-1, patch_size[1]-1, patch_size[2]-1
                        outputs = outputs[
                            :,
                            :,
                            i_ini:i_fin,
                            j_ini:j_fin,
                            k_ini:k_fin,
                        ]
                        aggregator.add_batch(outputs, locations)
    
                    output = aggregator.get_output_tensor()
                    assert (output == -5).all()
                    assert output.shape == self.sample_subject.t1.shape
    
    
    def model(tensor):
        tensor[:] = -5
        return tensor
    

    Actual outcome

    This raises a RuntimeError if patch_overlap is smaller than the difference between input and output, and the overlap mode is anything but crop

    Below is the output of running pytest tests/data/inference/test_inference_smaller.py

    Error messages

    ==================================================================================================== FAILURES =====================================================================================================
    _____________________________________________________________________________________ TestInference.test_inference_no_padding _____________________________________________________________________________________
    
    self = <tests.data.inference.test_inference_smaller.TestInference testMethod=test_inference_no_padding>
    
        def test_inference_no_padding(self):
    >       self.try_inference(None)
    
    test_inference_smaller.py:13: 
    _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
    test_inference_smaller.py:47: in try_inference
        aggregator.add_batch(outputs, locations)
    _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
    
    self = <torchio.data.inference.aggregator.GridAggregator object at 0x7f8353643bb0>
    batch_tensor = tensor([[[[[-5, -5, -5,  ..., -5, -5, -5],
               [-5, -5, -5,  ..., -5, -5, -5],
               [-5, -5, -5,  ..., -5..., -5, -5],
               [-5, -5, -5,  ..., -5, -5, -5],
               [-5, -5, -5,  ..., -5, -5, -5]]]]], dtype=torch.int32)
    locations = array([[ 0,  0,  0, 10, 15, 17],
           [ 0,  0, 13, 10, 15, 30],
           [ 0,  5,  0, 10, 20, 17],
           [ 0,  5, 13, 10, 20, 30]])
    
        def add_batch(
                self,
                batch_tensor: torch.Tensor,
                locations: torch.Tensor,
        ) -> None:
            """Add batch processed by a CNN to the output prediction volume.
        
            Args:
                batch_tensor: 5D tensor, typically the output of a convolutional
                    neural network, e.g. ``batch['image'][torchio.DATA]``.
                locations: 2D tensor with shape :math:`(B, 6)` representing the
                    patch indices in the original image. They are typically
                    extracted using ``batch[torchio.LOCATION]``.
            """
            batch = batch_tensor.cpu()
            locations = locations.cpu().numpy()
            patch_sizes = locations[:, 3:] - locations[:, :3]
            # There should be only one patch size
            assert len(np.unique(patch_sizes, axis=0)) == 1
            input_spatial_shape = tuple(batch.shape[-3:])
            target_spatial_shape = tuple(patch_sizes[0])
            if input_spatial_shape != target_spatial_shape:
                message = (
                    f'The shape of the input batch, {input_spatial_shape},'
                    ' does not match the shape of the target location,'
                    f' which is {target_spatial_shape}'
                )
    >           raise RuntimeError(message)
    E           RuntimeError: The shape of the input batch, (8, 13, 15), does not match the shape of the target location, which is (10, 15, 17)
    
    ../../../src/torchio/data/inference/aggregator.py:153: RuntimeError
    ______________________________________________________________________________________ TestInference.test_inference_padding _______________________________________________________________________________________
    
    self = <tests.data.inference.test_inference_smaller.TestInference testMethod=test_inference_padding>
    
        def test_inference_padding(self):
    >       self.try_inference(3)
    
    test_inference_smaller.py:16: 
    _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
    test_inference_smaller.py:47: in try_inference
        aggregator.add_batch(outputs, locations)
    _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
    
    self = <torchio.data.inference.aggregator.GridAggregator object at 0x7f835149ca90>
    batch_tensor = tensor([[[[[-5, -5, -5,  ..., -5, -5, -5],
               [-5, -5, -5,  ..., -5, -5, -5],
               [-5, -5, -5,  ..., -5..., -5, -5],
               [-5, -5, -5,  ..., -5, -5, -5],
               [-5, -5, -5,  ..., -5, -5, -5]]]]], dtype=torch.int32)
    locations = array([[ 0,  0,  0, 10, 15, 17],
           [ 0,  0, 13, 10, 15, 30],
           [ 0,  5,  0, 10, 20, 17],
           [ 0,  5, 13, 10, 20, 30]])
    
        def add_batch(
                self,
                batch_tensor: torch.Tensor,
                locations: torch.Tensor,
        ) -> None:
            """Add batch processed by a CNN to the output prediction volume.
        
            Args:
                batch_tensor: 5D tensor, typically the output of a convolutional
                    neural network, e.g. ``batch['image'][torchio.DATA]``.
                locations: 2D tensor with shape :math:`(B, 6)` representing the
                    patch indices in the original image. They are typically
                    extracted using ``batch[torchio.LOCATION]``.
            """
            batch = batch_tensor.cpu()
            locations = locations.cpu().numpy()
            patch_sizes = locations[:, 3:] - locations[:, :3]
            # There should be only one patch size
            assert len(np.unique(patch_sizes, axis=0)) == 1
            input_spatial_shape = tuple(batch.shape[-3:])
            target_spatial_shape = tuple(patch_sizes[0])
            if input_spatial_shape != target_spatial_shape:
                message = (
                    f'The shape of the input batch, {input_spatial_shape},'
                    ' does not match the shape of the target location,'
                    f' which is {target_spatial_shape}'
                )
    >           raise RuntimeError(message)
    E           RuntimeError: The shape of the input batch, (8, 13, 15), does not match the shape of the target location, which is (10, 15, 17)
    
    ../../../src/torchio/data/inference/aggregator.py:153: RuntimeError
    ================================================================================================ warnings summary =================================================================================================
    test_inference_smaller.py: 16 warnings
      /home/wahab/miniconda3/envs/torchioenv/lib/python3.10/site-packages/SimpleITK/extra.py:183: DeprecationWarning: Converting `np.character` to a dtype is deprecated. The current result is `np.dtype(np.str_)` which is not strictly correct. Note that `np.character` is generally deprecated and 'S1' should be used.
        _np_sitk = {np.dtype(np.character): sitkUInt8,
    
    -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
    ============================================================================================= short test summary info =============================================================================================
    FAILED test_inference_smaller.py::TestInference::test_inference_no_padding - RuntimeError: The shape of the input batch, (8, 13, 15), does not match the shape of the target location, which is (10, 15, 17)
    FAILED test_inference_smaller.py::TestInference::test_inference_padding - RuntimeError: The shape of the input batch, (8, 13, 15), does not match the shape of the target location, which is (10, 15, 17)
    ========================================================================================= 2 failed, 16 warnings in 0.97s =========================================================================================
    

    Expected outcome

    I believe tio should be able to handle smaller outputs. My model predictions are terrible even with averaging or hann windowing. Unfortunately most popular model libraries (such as the great monai) only provide models with the same output size and input. But it is crucial in my application to let the model see a bigger input ROI than semantic label outputs - by padding convolutions, as this gives context for the prediction. The original unet paper uses padded convolutions for smaller outputs than inputs.

    I am going to make a PR tomorrow to add a fix for this, my planned changes are to only change the aggregator. This can be fixed with only changes to GridAggregator and the sampler can be left the same :

    • [x] Check if the aggregator input is smaller than the sampler output in `GridAggregator.add_batch()' before comparing it to the location patch size
    • [x] Create a variable in aggregator called patch_diffs which is the difference between input_spatial_shape and target_spatial_shape
    • [x] Change each dimension of self.patch_overlap to patch_diffs if it is smaller
    • [ ] ~Edit each location before cropping by adding half the diffs from i_ini etc and removing half the diffs from i_fin~
    • [x] Write a new unit test (Let me know if this can be improved)

    If you see an issue with this happening behind the scenes, should model_output_size be added as an argument to GridAggreator or GridSampler? Or should Aggregator raise a warning if it detects it behind the scenes?

    This is a bit confusing even in the code as the models output is the aggregators input, I've tried to be clear here, let me know if I havent.

    System info

    Platform:   Linux-5.4.0-131-generic-x86_64-with-glibc2.27
    TorchIO:    0.18.86
    PyTorch:    1.13.0+cu117
    SimpleITK:  2.2.0 (ITK 5.3)
    NumPy:      1.23.4
    Python:     3.10.8 (main, Nov  4 2022, 13:48:29) [GCC 11.2.0]
    
    bug 
    opened by wahabk 1
  • Implemented reversible RescaleIntensity transform

    Implemented reversible RescaleIntensity transform

    Fixes #993 .

    Description The RescaleIntensity Tranasform constitutes a min-max transformation, which is easily reversible. As a result, the class attribute self.invert_transform is added and the function rescale is touched slightly in order to revert the transformation. As it would be helpful for the user to have access to the class attribute self.in_min_max (and the respective in_min, in_max), the respective attributes are added.

    Known Issues

    I checked the reversibility of the transform. If the resulting torch.Tensor after applying transform and transform.inverse() to a random input vector, a difference between input and output tensor results. I assume this originates from rounding issues between int and float. If the output tensor is kept as a float, the difference is 6.2028e-07 per pixel (max: 3.8147e-06 min: 3.8147e-06) for a 4x64x64x64 tensor. If the output tensor is casted to int, the difference becomes 0.2465 (max: 1. min: 0.)

    in_tensor = torch.randint(256, (4, 64, 64, 64))
    
    transform = torchio.transforms.preprocessing.RescaleIntensity(out_min_max=(-1,1))
    
    transformed = transform(in_tensor)
    reverted = transform.inverse()(transformed)
    
    diff = a-reverted.int()  # or without casting to int
    
    mean_ = torch.mean(diff.float())
    max_ = torch.max(diff.float())
    min_ = torch.min(diff.float())
    

    Checklist

    • [x] I have read the CONTRIBUTING docs and have a developer setup (especially important are pre-commitand pytest)
    • [x] Non-breaking change (would not break existing functionality)
    • [ ] Breaking change (would cause existing functionality to change)
    • [ ] Tests added or modified to cover the changes
    • [x] Integration tests passed locally by running pytest
      • I had a lot of warnings (510 passed, 3846 warnings), which I assume are not originating from my Code (only minor changes) but most likely from other parts of the projects
    • [ ] In-line docstrings updated
    • [ ] Documentation updated, tested running make html inside the docs/ folder
    • [x] This pull request is ready to be reviewed
    • [ ] If the PR is ready and there are multiple commits, I have squashed them and force-pushed
    opened by nicoloesch 0
Releases(v0.18.0)
  • v0.18.0(Nov 29, 2020)

  • v0.17.42(Sep 23, 2020)

    TorchIO is a Python package containing a set of tools to efficiently read, preprocess, sample, augment, and write 3D medical images in deep learning applications written in PyTorch, including intensity and spatial transforms for data augmentation and preprocessing. Transforms include typical computer vision operations such as random affine transformations and also domain-specific ones such as simulation of intensity artifacts due to MRI magnetic field inhomogeneity or k-space motion artifacts.

    Source code(tar.gz)
    Source code(zip)
  • v0.13.9(Mar 3, 2020)

    TorchIO is a Python package containing a set of tools to efficiently read, sample and write 3D medical images in deep learning applications written in PyTorch, including intensity and spatial transforms for data augmentation and preprocessing. Transforms include typical computer vision operations such as random affine transformations and also domain-specific ones such as simulation of intensity artifacts due to MRI magnetic field inhomogeneity or k-space motion artifacts.

    Source code(tar.gz)
    Source code(zip)
  • v0.12.9(Jan 28, 2020)

    TorchIO is a Python package containing a set of tools to efficiently read, sample and write 3D medical images in deep learning applications written in PyTorch, including intensity and spatial transforms for data augmentation and preprocessing. Transforms include typical computer vision operations such as random affine transformations and also domain-specific ones such as simulation of intensity artifacts due to MRI magnetic field inhomogeneity or k-space motion artifacts.

    Source code(tar.gz)
    Source code(zip)
  • v0.12.8(Jan 26, 2020)

    TorchIO is a Python package containing a set of tools to efficiently read, sample and write 3D medical images in deep learning applications written in PyTorch, including intensity and spatial transforms for data augmentation and preprocessing. Transforms include typical computer vision operations such as random affine transformations and also domain-specific ones such as simulation of intensity artifacts due to MRI magnetic field inhomogeneity or k-space motion artifacts.

    Source code(tar.gz)
    Source code(zip)
  • v0.11.2(Jan 15, 2020)

    TorchIO

    DOI PyPI version Build Status

    torchio is a Python package containing a set of tools to efficiently read, sample and write 3D medical images in deep learning applications written in PyTorch, including intensity and spatial transforms for data augmentation and preprocessing. Transforms include typical computer vision operations such as random affine transformations and also domain specific ones such as simulation of intensity artifacts due to MRI magnetic field inhomogeneity or k-space motion artifacts.

    This package has been greatly inspired by NiftyNet.

    Credits

    If you like this repository, please click on Star!

    If you used this package for your research, please cite this repository using the information available on its Zenodo entry or use this BibTeX:

    @software{perez_garcia_fernando_2020_3598622,
      author       = {Pérez-García, Fernando},
      title        = {{fepegar/torchio: TorchIO: Tools for loading,
                       augmenting and writing 3D medical images on
                       PyTorch}},
      month        = jan,
      year         = 2020,
      publisher    = {Zenodo},
      doi          = {10.5281/zenodo.3598622},
      url          = {https://doi.org/10.5281/zenodo.3598622}
    }
    

    Index

    Installation

    This package is on the Python Package Index (PyPI). To install it, just run in a terminal the following command:

    $ pip install torchio
    

    Features

    Data handling

    ImagesDataset

    ImagesDataset is a reader of medical images that directly inherits from torch.utils.Dataset. It can be used with a torch.utils.DataLoader for efficient reading and data augmentation.

    It receives a list of subjects, where each subject is composed of a list of torchio.Image instances. The paths suffix must be .nii, .nii.gz or .nrrd.

    import torchio
    
    subject_a = [
        Image('t1', '~/Dropbox/MRI/t1.nrrd', torchio.INTENSITY),
        Image('label', '~/Dropbox/MRI/t1_seg.nii.gz', torchio.LABEL),
    ]
    subject_b = [
        Image('t1', '/tmp/colin27_t1_tal_lin.nii.gz', torchio.INTENSITY),
        Image('t2', '/tmp/colin27_t2_tal_lin.nii', torchio.INTENSITY),
        Image('label', '/tmp/colin27_seg1.nii.gz', torchio.LABEL),
    ]
    subjects_list = [subject_a, subject_b]
    subjects_dataset = torchio.ImagesDataset(subjects_list)
    subject_sample = subjects_dataset[0]
    

    Samplers

    torchio includes grid, uniform and label patch samplers. There is also an aggregator used for dense predictions. For more information about patch-based training, see NiftyNet docs.

    import torch
    import torchio
    
    CHANNELS_DIMENSION = 1
    patch_overlap = 4
    grid_sampler = torchio.inference.GridSampler(
        input_array,  # some NumPy array
        patch_size=128,
        patch_overlap=patch_overlap,
    )
    patch_loader = torch.utils.data.DataLoader(grid_sampler, batch_size=4)
    aggregator = torchio.inference.GridAggregator(
        input_array,
        patch_overlap=patch_overlap,
    )
    
    with torch.no_grad():
        for patches_batch in patch_loader:
            input_tensor = patches_batch['image']
            locations = patches_batch['location']
            logits = model(input_tensor)  # some torch.nn.Module
            labels = logits.argmax(dim=CHANNELS_DIMENSION, keepdim=True)
            outputs = labels
            aggregator.add_batch(outputs, locations)
    
    output_array = aggregator.output_array
    

    Queue

    A patches Queue (or buffer) can be used for randomized patch-based sampling during training. This interactive animation can be used to understand how the queue works.

    import torch
    import torchio
    
    patches_queue = torchio.Queue(
        subjects_dataset=subjects_dataset,  # instance of torchio.ImagesDataset
        queue_length=300,
        samples_per_volume=10,
        patch_size=96,
        sampler_class=torchio.sampler.ImageSampler,
        num_workers=4,
        shuffle_subjects=True,
        shuffle_patches=True,
    )
    patches_loader = DataLoader(patches_queue, batch_size=4)
    
    num_epochs = 20
    for epoch_index in range(num_epochs):
        for patches_batch in patches_loader:
            logits = model(patches_batch)  # model is some torch.nn.Module
    

    Transforms

    The transforms package should remind users of torchvision.transforms. They take as input the samples generated by an ImagesDataset.

    A transform can be quickly applied to an image file using the command-line tool torchio-transform:

    $ torchio-transform input.nii.gz RandomMotion output.nii.gz --kwargs "proportion_to_augment=1 num_transforms=4"
    

    Augmentation

    Intensity
    MRI k-space motion artifacts

    Magnetic resonance images suffer from motion artifacts when the subject moves during image acquisition. This transform follows Shaw et al., 2019 to simulate motion artifacts for data augmentation.

    MRI k-space motion artifacts

    MRI magnetic field inhomogeneity

    MRI magnetic field inhomogeneity creates slow frequency intensity variations. This transform is very similar to the one in NiftyNet.

    MRI bias field artifacts

    Gaussian noise

    Adds noise sampled from a normal distribution with mean 0 and standard deviation sampled from a uniform distribution in the range std_range. It is often used after ZNormalization, as the output of this transform has zero-mean.

    Random Gaussian noise

    Spatial
    B-spline dense elastic deformation

    Random elastic deformation

    Flip

    Reverse the order of elements in an image along the given axes.

    Affine transform

    Preprocessing

    Histogram standardization

    Implementation of New variants of a method of MRI scale standardization adapted from NiftyNet.

    Histogram standardization

    Z-normalization

    This transform first extracts the values with intensity greater than the mean, which is an approximation of the foreground voxels. Then the foreground mean is subtracted from the image and it is divided by the foreground standard deviation.

    Z-normalization

    Rescale

    Rescale intensity values in an image to a certain range.

    Resample

    Resample images to a new voxel spacing using nibabel.

    Pad

    Pad images, like in torchvision.transforms.Pad.

    Crop

    Crop images passing 1, 3, or 6 integers, as in Pad.

    Example

    This example shows the improvement in performance when multiple workers are used to load and preprocess the volumes using multiple workers.

    import time
    import multiprocessing as mp
    
    from tqdm import trange
    
    import torch.nn as nn
    from torch.utils.data import DataLoader
    from torchvision.transforms import Compose
    
    from torchio import ImagesDataset, Queue, DATA
    from torchio.sampler import ImageSampler
    from torchio.utils import create_dummy_dataset
    from torchio.transforms import (
        ZNormalization,
        RandomNoise,
        RandomFlip,
        RandomAffine,
    )
    
    
    # Define training and patches sampling parameters
    num_epochs = 4
    patch_size = 128
    queue_length = 400
    samples_per_volume = 10
    batch_size = 4
    
    class Network(nn.Module):
        def __init__(self):
            super().__init__()
            self.conv = nn.Conv3d(
                in_channels=1,
                out_channels=3,
                kernel_size=3,
            )
        def forward(self, x):
            return self.conv(x)
    
    model = Network()
    
    # Create a dummy dataset in the temporary directory, for this example
    subjects_list = create_dummy_dataset(
        num_images=100,
        size_range=(193, 229),
        force=False,
    )
    
    # Each element of subjects_list is a dictionary:
    # subject_images = [
    #     torchio.Image('one_image', path_to_one_image, torchio.INTENSITY),
    #     torchio.Image('another_image', path_to_another_image, torchio.INTENSITY),
    #     torchio.Image('a_label', path_to_a_label, torchio.LABEL),
    # ]
    
    # Define transforms for data normalization and augmentation
    transforms = (
        ZNormalization(),
        RandomNoise(std_range=(0, 0.25)),
        RandomAffine(scales=(0.9, 1.1), degrees=10),
        RandomFlip(axes=(0,)),
    )
    transform = Compose(transforms)
    subjects_dataset = ImagesDataset(subjects_list, transform)
    
    
    # Run a benchmark for different numbers of workers
    workers = range(mp.cpu_count() + 1)
    for num_workers in workers:
        print('Number of workers:', num_workers)
    
        # Define the dataset as a queue of patches
        queue_dataset = Queue(
            subjects_dataset,
            queue_length,
            samples_per_volume,
            patch_size,
            ImageSampler,
            num_workers=num_workers,
        )
        batch_loader = DataLoader(queue_dataset, batch_size=batch_size)
    
        start = time.time()
        for epoch_index in trange(num_epochs, leave=False):
            for batch in batch_loader:
                # The keys of batch have been defined in create_dummy_dataset()
                inputs = batch['one_modality'][DATA]
                targets = batch['segmentation'][DATA]
                logits = model(inputs)
        print('Time:', int(time.time() - start), 'seconds')
        print()
    

    Output:

    Number of workers: 0
    Time: 394 seconds
    
    Number of workers: 1
    Time: 372 seconds
    
    Number of workers: 2
    Time: 278 seconds
    
    Number of workers: 3
    Time: 259 seconds
    
    Number of workers: 4
    Time: 242 seconds
    

    Related projects

    See also

    Source code(tar.gz)
    Source code(zip)
  • v0.8.0(Jan 9, 2020)

    TorchIO

    PyPI version DOI

    torchio is a Python package containing a set of tools to efficiently read, sample and write 3D medical images in deep learning applications written in PyTorch, including intensity and spatial transforms for data augmentation and preprocessing. Transforms include typical computer vision operations such as random affine transformations and also domain specific ones such as simulation of intensity artifacts due to MRI magnetic field inhomogeneity or k-space motion artifacts.

    This package has been greatly inspired by NiftyNet.

    Index

    Installation

    $ pip install torchio
    

    Features

    Data handling

    ImagesDataset

    ImagesDataset is a reader of medical images that directly inherits from torch.utils.Dataset. It can be used with a torch.utils.DataLoader for efficient reading and data augmentation.

    The paths suffix must be .nii, .nii.gz or .nrrd.

    import torchio
    
    subject_a = {
        't1': dict(path='~/Dropbox/MRI/t1.nii.gz', type=torchio.INTENSITY),
        'label': dict(path='~/Dropbox/MRI/t1_seg.nii.gz', type=torchio.LABEL),
    }
    subject_b = {
        't1': dict(path='/tmp/colin27_t1_tal_lin.nii.gz', type=torchio.INTENSITY),
        'label': dict(path='/tmp/colin27_seg1.nii.gz', type=torchio.LABEL),
    }
    paths_list = [subject_a, subject_b]
    subjects_dataset = torchio.ImagesDataset(paths_list)
    subject_sample = subjects_dataset[0]
    

    Samplers

    torchio includes grid, uniform and label patch samplers. There is also an aggregator used for dense predictions. The code for these is almost copy-pasted from NiftyNet.

    For more information about patch-based training, see NiftyNet docs.

    import torch
    import torchio
    
    CHANNELS_DIMENSION = 1
    patch_overlap = 4
    grid_sampler = torchio.inference.GridSampler(
        input_array,  # some NumPy array
        patch_size=128,
        patch_overlap=patch_overlap,
    )
    patch_loader = torch.utils.data.DataLoader(grid_sampler, batch_size=4)
    aggregator = torchio.inference.GridAggregator(
        input_array,
        patch_overlap=patch_overlap,
    )
    
    with torch.no_grad():
        for patches_batch in patch_loader:
            input_tensor = patches_batch['image']
            locations = patches_batch['location']
            logits = model(input_tensor)  # some torch.nn.Module
            labels = logits.argmax(dim=CHANNELS_DIMENSION, keepdim=True)
            outputs = labels
            aggregator.add_batch(outputs, locations)
    
    output_array = aggregator.output_array
    

    Queue

    A patches Queue (or buffer) can be used for randomized patch-based sampling during training. This interactive animation can be used to understand how the queue works.

    import torch
    import torchio
    
    patches_queue = torchio.Queue(
        subjects_dataset=subjects_dataset,
        queue_length=300,
        samples_per_volume=10,
        patch_size=96,
        sampler_class=torchio.sampler.ImageSampler,
        num_workers=4,
        shuffle_subjects=True,
        shuffle_patches=True,
    )
    patches_loader = DataLoader(patches_queue, batch_size=4)
    
    num_epochs = 20
    for epoch_index in range(num_epochs):
        for patches_batch in patches_loader:
            logits = model(patches_batch)  # model is some torch.nn.Module
    

    Transforms

    The transforms package should remind users of torchvision.transforms. They take as input the samples generated by an ImagesDataset.

    Intensity

    MRI k-space motion artifacts

    Magnetic resonance images suffer from motion artifacts when the subject moves during image acquisition. This transform follows Shaw et al., 2019 to simulate motion artifacts for data augmentation.

    MRI k-space motion artifacts

    MRI magnetic field inhomogeneity

    MRI magnetic field inhomogeneity creates slow frequency intensity variations. This transform is very similar to the one in NiftyNet.

    MRI bias field artifacts

    Gaussian noise

    Adds noise sampled from a normal distribution with mean 0 and standard deviation sampled from a uniform distribution in the range std_range. It is often used after ZNormalization, as the output of this transform has zero-mean.

    Random Gaussian noise

    Normalization
    Histogram standardization

    Implementation of New variants of a method of MRI scale standardization adapted from NiftyNet.

    Histogram standardization

    Z-normalization

    This transform first extracts the values with intensity greater than the mean, which is an approximation of the foreground voxels. Then the foreground mean is subtracted from the image and it is divided by the foreground standard deviation.

    Z-normalization

    Rescale

    Spatial

    Flip

    Reverse the order of elements in an image along the given axes.

    Affine transform
    B-spline dense elastic deformation

    Random elastic deformation

    Example

    This example shows the improvement in performance when multiple workers are used to load and preprocess the volumes using multiple workers.

    import time
    import multiprocessing as mp
    
    from tqdm import trange
    
    import torch.nn as nn
    from torch.utils.data import DataLoader
    from torchvision.transforms import Compose
    
    from torchio import ImagesDataset, Queue
    from torchio.sampler import ImageSampler
    from torchio.utils import create_dummy_dataset
    from torchio.transforms import (
        ZNormalization,
        RandomNoise,
        RandomFlip,
        RandomAffine,
    )
    
    
    # Define training and patches sampling parameters
    num_epochs = 4
    patch_size = 128
    queue_length = 400
    samples_per_volume = 10
    batch_size = 4
    
    class Network(nn.Module):
        def __init__(self):
            super().__init__()
            self.conv = nn.Conv3d(
                in_channels=1,
                out_channels=3,
                kernel_size=3,
            )
        def forward(self, x):
            return self.conv(x)
    
    model = Network()
    
    # Create a dummy dataset in the temporary directory, for this example
    subjects_list = create_dummy_dataset(
        num_images=100,
        size_range=(193, 229),
        force=False,
    )
    
    # Each element of subjects_list is a dictionary:
    # subject_images = [
    #     torchio.Image('one_image', path_to_one_image, torchio.INTENSITY),
    #     torchio.Image('another_image', path_to_another_image, torchio.INTENSITY),
    #     torchio.Image('a_label', path_to_a_label, torchio.LABEL),
    # ]
    
    # Define transforms for data normalization and augmentation
    transforms = (
        ZNormalization(),
        RandomNoise(std_range=(0, 0.25)),
        RandomAffine(scales=(0.9, 1.1), degrees=10),
        RandomFlip(axes=(0,)),
    )
    transform = Compose(transforms)
    subjects_dataset = ImagesDataset(subjects_list, transform)
    
    
    # Run a benchmark for different numbers of workers
    workers = range(mp.cpu_count() + 1)
    for num_workers in workers:
        print('Number of workers:', num_workers)
    
        # Define the dataset as a queue of patches
        queue_dataset = Queue(
            subjects_dataset,
            queue_length,
            samples_per_volume,
            patch_size,
            ImageSampler,
            num_workers=num_workers,
        )
        batch_loader = DataLoader(queue_dataset, batch_size=batch_size)
    
        start = time.time()
        for epoch_index in trange(num_epochs, leave=False):
            for batch in batch_loader:
                # The keys of batch have been defined in create_dummy_dataset()
                inputs = batch['one_modality']['data']
                targets = batch['segmentation']['data']
                logits = model(inputs)
        print('Time:', int(time.time() - start), 'seconds')
        print()
    

    Output:

    Number of workers: 0
    Time: 394 seconds
    
    Number of workers: 1
    Time: 372 seconds
    
    Number of workers: 2
    Time: 278 seconds
    
    Number of workers: 3
    Time: 259 seconds
    
    Number of workers: 4
    Time: 242 seconds
    
    Source code(tar.gz)
    Source code(zip)
  • v0.7.8(Jan 8, 2020)

    TorchIO

    PyPI version DOI

    torchio is a Python package containing a set of tools to efficiently read, sample and write 3D medical images in deep learning applications written in PyTorch, including intensity and spatial transforms for data augmentation and preprocessing. Transforms include typical computer vision operations such as random affine transformations and also domain specific ones such as simulation of intensity artifacts due to MRI magnetic field inhomogeneity or k-space motion artifacts.

    This package has been greatly inspired by NiftyNet.

    Index

    Installation

    $ pip install torchio
    

    Features

    Data handling

    ImagesDataset

    ImagesDataset is a reader of medical images that directly inherits from torch.utils.Dataset. It can be used with a torch.utils.DataLoader for efficient reading and data augmentation.

    The paths suffix must be .nii, .nii.gz or .nrrd.

    import torchio
    
    subject_a = {
        't1': dict(path='~/Dropbox/MRI/t1.nii.gz', type=torchio.INTENSITY),
        'label': dict(path='~/Dropbox/MRI/t1_seg.nii.gz', type=torchio.LABEL),
    }
    subject_b = {
        't1': dict(path='/tmp/colin27_t1_tal_lin.nii.gz', type=torchio.INTENSITY),
        'label': dict(path='/tmp/colin27_seg1.nii.gz', type=torchio.LABEL),
    }
    paths_list = [subject_a, subject_b]
    subjects_dataset = torchio.ImagesDataset(paths_list)
    subject_sample = subjects_dataset[0]
    

    Samplers

    torchio includes grid, uniform and label patch samplers. There is also an aggregator used for dense predictions. The code for these is almost copy-pasted from NiftyNet.

    For more information about patch-based training, see NiftyNet docs.

    import torch
    import torchio
    
    CHANNELS_DIMENSION = 1
    patch_overlap = 4
    grid_sampler = torchio.inference.GridSampler(
        input_array,  # some NumPy array
        patch_size=128,
        patch_overlap=patch_overlap,
    )
    patch_loader = torch.utils.data.DataLoader(grid_sampler, batch_size=4)
    aggregator = torchio.inference.GridAggregator(
        input_array,
        patch_overlap=patch_overlap,
    )
    
    with torch.no_grad():
        for patches_batch in patch_loader:
            input_tensor = patches_batch['image']
            locations = patches_batch['location']
            logits = model(input_tensor)  # some torch.nn.Module
            labels = logits.argmax(dim=CHANNELS_DIMENSION, keepdim=True)
            outputs = labels
            aggregator.add_batch(outputs, locations)
    
    output_array = aggregator.output_array
    

    Queue

    A patches Queue (or buffer) can be used for randomized patch-based sampling during training. This interactive animation can be used to understand how the queue works.

    import torch
    import torchio
    
    patches_queue = torchio.Queue(
        subjects_dataset=subjects_dataset,
        queue_length=300,
        samples_per_volume=10,
        patch_size=96,
        sampler_class=torchio.sampler.ImageSampler,
        num_workers=4,
        shuffle_subjects=True,
        shuffle_patches=True,
    )
    patches_loader = DataLoader(patches_queue, batch_size=4)
    
    num_epochs = 20
    for epoch_index in range(num_epochs):
        for patches_batch in patches_loader:
            logits = model(patches_batch)  # model is some torch.nn.Module
    

    Transforms

    The transforms package should remind users of torchvision.transforms. They take as input the samples generated by an ImagesDataset.

    Intensity

    MRI k-space motion artifacts

    Magnetic resonance images suffer from motion artifacts when the subject moves during image acquisition. This transform follows Shaw et al., 2019 to simulate motion artifacts for data augmentation.

    MRI k-space motion artifacts

    MRI magnetic field inhomogeneity

    MRI magnetic field inhomogeneity creates slow frequency intensity variations. This transform is very similar to the one in NiftyNet.

    MRI bias field artifacts

    Gaussian noise

    Adds noise sampled from a normal distribution with mean 0 and standard deviation sampled from a uniform distribution in the range std_range. It is often used after ZNormalization, as the output of this transform has zero-mean.

    Random Gaussian noise

    Normalization
    Histogram standardization

    Implementation of New variants of a method of MRI scale standardization adapted from NiftyNet.

    Histogram standardization

    Z-normalization
    Rescale

    Spatial

    Flip

    Reverse the order of elements in an image along the given axes.

    Affine transform
    B-spline dense elastic deformation

    Random elastic deformation

    Example

    This example shows the improvement in performance when multiple workers are used to load and preprocess the volumes using multiple workers.

    import time
    import multiprocessing as mp
    
    from torch.utils.data import DataLoader
    from torchvision.transforms import Compose
    
    from torchio import ImagesDataset, Queue
    from torchio.sampler import ImageSampler
    from torchio.utils import create_dummy_dataset
    from torchio.transforms import (
        ZNormalization,
        RandomNoise,
        RandomFlip,
        RandomAffine,
    )
    
    
    # Define training and patches sampling parameters
    num_epochs = 4
    patch_size = 128
    queue_length = 100
    samples_per_volume = 10
    batch_size = 4
    
    def model(batch, sleep_time=0.1):
        """Dummy function to simulate a forward pass through the network"""
        time.sleep(sleep_time)
        return batch
    
    # Create a dummy dataset in the temporary directory, for this example
    subjects_paths = create_dummy_dataset(
        num_images=100,
        size_range=(193, 229),
        force=False,
    )
    
    # Each element of subjects_paths is a dictionary:
    # subject = {
    #     'one_image': dict(path=path_to_one_image, type=torchio.INTENSITY),
    #     'another_image': dict(path=path_to_another_image, type=torchio.INTENSITY),
    #     'a_label': dict(path=path_to_a_label, type=torchio.LABEL),
    # }
    
    # Define transforms for data normalization and augmentation
    transforms = (
        ZNormalization(),
        RandomNoise(std_range=(0, 0.25)),
        RandomAffine(scales=(0.9, 1.1), degrees=10),
        RandomFlip(axes=(0,)),
    )
    transform = Compose(transforms)
    subjects_dataset = ImagesDataset(subjects_paths, transform)
    
    sample = subjects_dataset[0]
    
    # Run a benchmark for different numbers of workers
    workers = range(mp.cpu_count() + 1)
    for num_workers in workers:
        print('Number of workers:', num_workers)
    
        # Define the dataset as a queue of patches
        queue_dataset = Queue(
            subjects_dataset,
            queue_length,
            samples_per_volume,
            patch_size,
            ImageSampler,
            num_workers=num_workers,
        )
        batch_loader = DataLoader(queue_dataset, batch_size=batch_size)
    
        start = time.time()
        for epoch_index in range(num_epochs):
            for batch in batch_loader:
                logits = model(batch)
        print('Time:', int(time.time() - start), 'seconds')
        print()
    

    Output:

    Number of workers: 0
    Time: 394 seconds
    
    Number of workers: 1
    Time: 372 seconds
    
    Number of workers: 2
    Time: 278 seconds
    
    Number of workers: 3
    Time: 259 seconds
    
    Number of workers: 4
    Time: 242 seconds
    
    Source code(tar.gz)
    Source code(zip)
  • v0.7.0(Jan 6, 2020)

    TorchIO

    PyPI version

    torchio is a Python package containing a set of tools to efficiently read, sample and write 3D medical images in deep learning applications written in PyTorch, including intensity and spatial transforms for data augmentation. Transforms include typical computer vision operations such as random affine transformations and also domain specific ones such as simulation of intensity artifacts due to MRI magnetic field inhomogeneity or k-space motion artifacts.

    This package has been greatly inspired by NiftyNet.

    Index

    Installation

    $ pip install torchio
    

    Features

    Data handling

    ImagesDataset

    ImagesDataset is a reader of medical images that directly inherits from torch.utils.Dataset. It can be used with a torch.utils.DataLoader for efficient reading and data augmentation.

    The paths suffix must be .nii, .nii.gz or .nrrd.

    import torchio
    
    subject_a = {
        't1': dict(path='~/Dropbox/MRI/t1.nii.gz', type=torchio.INTENSITY),
        'label': dict(path='~/Dropbox/MRI/t1_seg.nii.gz', type=torchio.LABEL),
    }
    subject_b = {
        't1': dict(path='/tmp/colin27_t1_tal_lin.nii.gz', type=torchio.INTENSITY),
        'label': dict(path='/tmp/colin27_seg1.nii.gz', type=torchio.LABEL),
    }
    paths_list = [subject_a, subject_b]
    subjects_dataset = torchio.ImagesDataset(paths_list)
    subject_sample = subjects_dataset[0]
    

    Samplers

    torchio includes grid, uniform and label patch samplers. There is also an aggregator used for dense predictions. The code for these is almost copy-pasted from NiftyNet.

    For more information about patch-based training, see NiftyNet docs.

    import torch
    import torchio
    
    CHANNELS_DIMENSION = 1
    patch_overlap = 4
    grid_sampler = torchio.inference.GridSampler(
        input_array,  # some NumPy array
        patch_size=128,
        patch_overlap=patch_overlap,
    )
    patch_loader = torch.utils.data.DataLoader(grid_sampler, batch_size=4)
    aggregator = torchio.inference.GridAggregator(
        input_array,
        patch_overlap=patch_overlap,
    )
    
    with torch.no_grad():
        for patches_batch in patch_loader:
            input_tensor = patches_batch['image']
            locations = patches_batch['location']
            logits = model(input_tensor)  # some torch.nn.Module
            labels = logits.argmax(dim=CHANNELS_DIMENSION, keepdim=True)
            outputs = labels
            aggregator.add_batch(outputs, locations)
    
    output_array = aggregator.output_array
    

    Queue

    A patches Queue (or buffer) can be used for randomized patch-based sampling during training. This interactive animation can be used to understand how the queue works.

    import torch
    import torchio
    
    patches_queue = torchio.Queue(
        subjects_dataset=subjects_dataset,
        queue_length=300,
        samples_per_volume=10,
        patch_size=96,
        sampler_class=torchio.sampler.ImageSampler,
        num_workers=4,
        shuffle_subjects=True,
        shuffle_patches=True,
    )
    patches_loader = DataLoader(patches_queue, batch_size=4)
    
    num_epochs = 20
    for epoch_index in range(num_epochs):
        for patches_batch in patches_loader:
            logits = model(patches_batch)  # model is some torch.nn.Module
    

    Transforms

    The transforms package should remind users of torchvision.transforms. They take as input the samples generated by an ImagesDataset.

    Intensity

    MRI k-space motion artifacts

    Magnetic resonance images suffer from motion artifacts when the subject moves during image acquisition. This transform follows Shaw et al., 2019 to simulate motion artifacts for data augmentation.

    MRI k-space motion artifacts

    MRI magnetic field inhomogeneity

    MRI magnetic field inhomogeneity creates slow frequency intensity variations. This transform is very similar to the one in NiftyNet.

    MRI bias field artifacts

    Gaussian noise

    Adds noise sampled from a normal distribution with mean 0 and standard deviation sampled from a uniform distribution in the range std_range. It is often used after ZNormalization, as the output of this transform has zero-mean.

    Random Gaussian noise

    Normalization
    Histogram standardization

    Implementation of New variants of a method of MRI scale standardization adapted from NiftyNet.

    Histogram standardization

    Z-normalization
    Rescale

    Spatial

    Flip

    Reverse the order of elements in an image along the given axes.

    Affine transform
    B-spline dense elastic deformation

    Random elastic deformation

    Example

    This example shows the improvement in performance when multiple workers are used to load and preprocess the volumes using multiple workers.

    import time
    import multiprocessing as mp
    
    from torch.utils.data import DataLoader
    from torchvision.transforms import Compose
    
    from torchio import ImagesDataset, Queue
    from torchio.sampler import ImageSampler
    from torchio.utils import create_dummy_dataset
    from torchio.transforms import (
        ZNormalization,
        RandomNoise,
        RandomFlip,
        RandomAffine,
    )
    
    
    # Define training and patches sampling parameters
    num_epochs = 4
    patch_size = 128
    queue_length = 100
    samples_per_volume = 10
    batch_size = 4
    
    def model(batch, sleep_time=0.1):
        """Dummy function to simulate a forward pass through the network"""
        time.sleep(sleep_time)
        return batch
    
    # Create a dummy dataset in the temporary directory, for this example
    subjects_paths = create_dummy_dataset(
        num_images=100,
        size_range=(193, 229),
        force=False,
    )
    
    # Each element of subjects_paths is a dictionary:
    # subject = {
    #     'one_image': dict(path=path_to_one_image, type=torchio.INTENSITY),
    #     'another_image': dict(path=path_to_another_image, type=torchio.INTENSITY),
    #     'a_label': dict(path=path_to_a_label, type=torchio.LABEL),
    # }
    
    # Define transforms for data normalization and augmentation
    transforms = (
        ZNormalization(),
        RandomNoise(std_range=(0, 0.25)),
        RandomAffine(scales=(0.9, 1.1), degrees=10),
        RandomFlip(axes=(0,)),
    )
    transform = Compose(transforms)
    subjects_dataset = ImagesDataset(subjects_paths, transform)
    
    sample = subjects_dataset[0]
    
    # Run a benchmark for different numbers of workers
    workers = range(mp.cpu_count() + 1)
    for num_workers in workers:
        print('Number of workers:', num_workers)
    
        # Define the dataset as a queue of patches
        queue_dataset = Queue(
            subjects_dataset,
            queue_length,
            samples_per_volume,
            patch_size,
            ImageSampler,
            num_workers=num_workers,
        )
        batch_loader = DataLoader(queue_dataset, batch_size=batch_size)
    
        start = time.time()
        for epoch_index in range(num_epochs):
            for batch in batch_loader:
                logits = model(batch)
        print('Time:', int(time.time() - start), 'seconds')
        print()
    

    Output:

    Number of workers: 0
    Time: 394 seconds
    
    Number of workers: 1
    Time: 372 seconds
    
    Number of workers: 2
    Time: 278 seconds
    
    Number of workers: 3
    Time: 259 seconds
    
    Number of workers: 4
    Time: 242 seconds
    
    Source code(tar.gz)
    Source code(zip)
Owner
Fernando Pérez-García
PhD student in Medical Imaging at University College London and King's College London.
Fernando Pérez-García
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