A deep-learning pipeline for segmentation of ambiguous microscopic images.

Overview

Welcome to

deepflash2

Official repository of deepflash2 - a deep-learning pipeline for segmentation of ambiguous microscopic images.

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Quick Start in 30 seconds

Colab

setup5.mov

Why using deepflash2?

The best of two worlds: Combining state of the art deep learning with a barrier free environment for life science researchers.

  • End-to-end process for life science researchers
    • graphical user interface - no coding skills required
    • free usage on Google Colab at no costs
    • easy deployment on own hardware
  • Reliable prediction on new data
    • Quality assurance and out-of-distribution detection

Kaggle Gold Medal and Innovation Price Winner

deepflash2 does not only work on fluorescent labels. The deepflash2 API built the foundation for winning the Innovation Award a Kaggle Gold Medal in the HuBMAP - Hacking the Kidney challenge. Have a look at our solution

Gold Medal

Citing

The preprint of our paper is available on arXiv. Please cite

@misc{griebel2021deepflash2,
    title={Deep-learning in the bioimaging wild: Handling ambiguous data with deepflash2}, 
    author={Matthias Griebel and Dennis Segebarth and Nikolai Stein and Nina Schukraft and Philip Tovote and Robert Blum and Christoph M. Flath},
    year={2021},
    eprint={2111.06693}
}

Installing

You can use deepflash2 by using Google Colab. You can run every page of the documentation as an interactive notebook - click "Open in Colab" at the top of any page to open it.

  • Be sure to change the Colab runtime to "GPU" to have it run fast!
  • Use Firefox or Google Chrome if you want to upload your images.

You can install deepflash2 on your own machines with conda (highly recommended):

conda install -c fastchan -c matjesg deepflash2 

To install with pip, use

pip install deepflash2

If you install with pip, you should install PyTorch first by following the installation instructions of pytorch or fastai.

Using Docker

Docker images for deepflash2 are built on top of the latest pytorch image and fastai images. You must install Nvidia-Docker to enable gpu compatibility with these containers.

  • CPU only

docker run -p 8888:8888 matjesg/deepflash

  • With GPU support (Nvidia-Docker must be installed.) has an editable install of fastai and fastcore.

docker run --gpus all -p 8888:8888 matjesg/deepflash All docker containers are configured to start a jupyter server. deepflash2 notebooks are available in the deepflash2_notebooks folder.

For more information on how to run docker see docker orientation and setup and fastai docker.

Creating segmentation masks with Fiji/ImageJ

If you don't have labelled training data available, you can use this instruction manual for creating segmentation maps. The ImagJ-Macro is available here.

Acronym

A Deep-learning pipeline for Fluorescent Label Segmentation that learns from Human experts

Comments
  • Ensembling Issue

    Ensembling Issue

    @matjesg
    There comes another issue which is while Ensembling of two models. Some how there is Either a Memory Leak or GPU RAM leak so our Sub are failing. we are clueless on this. This succeeded once but now not succeeding at all. any help would be appreciated.

    We use Ensembler Model that does mean of predictions of model we want to ensemble. Single model works fine. two models ensembling fails.

    1. How can we use Ensemble Learner class.Willl it help
    opened by jaideep11061982 5
  • Approach for Cross Validation and Seeding

    Approach for Cross Validation and Seeding

    Thanks for this wonderful framework.

    It would be really helpful if you can help me with an approach for K-fold cross validation.

    With the help of classes RandomTileDataset and TileDataset, I'm able to sample tiles from tiffle files but, how can I validate by performing k-fold cross validation over the entire training data picking some validation tiles in the first round of training and then picking the other tiles in the subsequent round of training without leakage of data between multiple validation folds.

    train_ds = RandomTileDataset(files, ...)
    valid_ds = TileDataset(files, ...)
    

    Also, please let me know if RandomTileDataset and TileDataset picks tiles without leakage between two datasets. That is, are both the splits exclusive of each other? Is there a chance of some of the validation tiles picked by TileDataset to be present in the random tiles picked by RandomTileDataset

    On a related note, please help me with the required seeds that should be set for the entire pipeline with steps such as dataset/data loader preparation, training, inference, etc.

    Thanks in advance.

    opened by sreevishnu-damodaran 2
  • initial commit new features

    initial commit new features

    New features

    • Multiclass GT Estimation, closes #34
    • Torchscript ensemble class for inference / tta adjusted
    • ONNX export possible

    Major changes

    • Different classes for training (EnsembleLearner) and Inference (EnsemblePredictor)
    • Normalization based on uin8 images (0...255)
    enhancement 
    opened by matjesg 1
  • Add train and predict tutorial

    Add train and predict tutorial

    Add notebook tutorial_train_and_pred.ipynb to give users an introduction into codebased training and prediction for example data from google-drive, zip files or own directory. Available example datasets: "PV_in_HC", "cFOS_in_HC", "mScarlet_in_PAG", "YFP_in_CTX", "GFAP_in_HC"

    opened by nicoelbert 1
  • Error in opening pdf and labels

    Error in opening pdf and labels

    Hi there , thanx for this wonderful library i am getting this error is it because of the directory management i think , i have a dataset of masks where i have sampled it to pdf and the labels but they are on separate directory but have a same root folder , i am not able to create a tiledataset using your RandomTileDataset or TileDataset the path that i am using is '/content/masks_scale2/' and it has two sub folders labels and pdf

    /usr/local/lib/python3.7/dist-packages/deepflash2/data.py in _read_msk(path, n_classes, instance_labels, **kwargs) 268 msk = tifffile.imread(path, **kwargs) 269 else: --> 270 msk = imageio.imread(path, **kwargs) 271 if not instance_labels: 272 if np.max(msk)>n_classes:

    /usr/local/lib/python3.7/dist-packages/imageio/core/functions.py in imread(uri, format, **kwargs) 219 220 # Get reader and read first --> 221 reader = read(uri, format, "i", **kwargs) 222 with reader: 223 return reader.get_data(0)

    /usr/local/lib/python3.7/dist-packages/imageio/core/functions.py in get_reader(uri, format, mode, **kwargs) 137 if format is None: 138 raise ValueError( --> 139 "Could not find a format to read the specified file " "in mode %r" % mode 140 ) ValueError: Could not find a format to read the specified file in mode 'i'

    opened by mu745511 1
  • Prediction not possible on Windows machines due to cuda error

    Prediction not possible on Windows machines due to cuda error

    During prediction on a windows machine a a OS Error occurs due to:

    "RuntimeError: cuda runtime error (801) : operation not supported at ..\torch/csrc/generic/StorageSharing.cpp:247"

    Problem: Storage sharing currently not supported on windows.

    Proposed solution: Ensemble learner takes "num_workers" argument and passes it to subsequent functions. If num_workers == 0, prediction works for me.

    bug 
    opened by Maddonix 1
  • Type Error when starting training

    Type Error when starting training

    When I try to start the training process in google colab, this error occurs:

    TypeError: no implementation found for 'torch.nn.functional.cross_entropy' on types that implement torch_function: [<class 'fastai.torch_core.TensorImage'>, <class 'fastai.torch_core.TensorMask'>]

    as well as

    FileNotFoundError: [Errno 2] No such file or directory: 'models/model.pth'

    in the end.

    Hope you know whats the problem.

    bug 
    opened by AmSchulte 1
  • Groundtruth calculation is broken since update of simpleITK

    Groundtruth calculation is broken since update of simpleITK

    The calculation of the ground truth is broken since the release of simpleITK 2.0

    Solution is to add "!pip install SimpleITK==1.2.4" manually to the google colab. Maybe it would be good to add a requirements.txt to the installation and add an upper version bound to the installed dependencies.

    opened by AmSchulte 1
  • Bump nokogiri from 1.13.9 to 1.13.10 in /docs

    Bump nokogiri from 1.13.9 to 1.13.10 in /docs

    Bumps nokogiri from 1.13.9 to 1.13.10.

    Release notes

    Sourced from nokogiri's releases.

    1.13.10 / 2022-12-07

    Security

    • [CRuby] Address CVE-2022-23476, unchecked return value from xmlTextReaderExpand. See GHSA-qv4q-mr5r-qprj for more information.

    Improvements

    • [CRuby] XML::Reader#attribute_hash now returns nil on parse errors. This restores the behavior of #attributes from v1.13.7 and earlier. [#2715]

    sha256 checksums:

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    d3ee00f26c151763da1691c7fc6871ddd03e532f74f85101f5acedc2d099e958  nokogiri-1.13.10.gem
    
    Changelog

    Sourced from nokogiri's changelog.

    1.13.10 / 2022-12-07

    Security

    • [CRuby] Address CVE-2022-23476, unchecked return value from xmlTextReaderExpand. See GHSA-qv4q-mr5r-qprj for more information.

    Improvements

    • [CRuby] XML::Reader#attribute_hash now returns nil on parse errors. This restores the behavior of #attributes from v1.13.7 and earlier. [#2715]
    Commits
    • 4c80121 version bump to v1.13.10
    • 85410e3 Merge pull request #2715 from sparklemotion/flavorjones-fix-reader-error-hand...
    • 9fe0761 fix(cruby): XML::Reader#attribute_hash returns nil on error
    • 3b9c736 Merge pull request #2717 from sparklemotion/flavorjones-lock-psych-to-fix-bui...
    • 2efa87b test: skip large cdata test on system libxml2
    • 3187d67 dep(dev): pin psych to v4 until v5 builds in CI
    • a16b4bf style(rubocop): disable Minitest/EmptyLineBeforeAssertionMethods
    • See full diff in compare view

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    dependencies 
    opened by dependabot[bot] 0
  • Bump nokogiri from 1.13.6 to 1.13.9 in /docs

    Bump nokogiri from 1.13.6 to 1.13.9 in /docs

    Bumps nokogiri from 1.13.6 to 1.13.9.

    Release notes

    Sourced from nokogiri's releases.

    1.13.9 / 2022-10-18

    Security

    Dependencies

    • [CRuby] Vendored libxml2 is updated to v2.10.3 from v2.9.14.
    • [CRuby] Vendored libxslt is updated to v1.1.37 from v1.1.35.
    • [CRuby] Vendored zlib is updated from 1.2.12 to 1.2.13. (See LICENSE-DEPENDENCIES.md for details on which packages redistribute this library.)

    Fixed

    • [CRuby] Nokogiri::XML::Namespace objects, when compacted, update their internal struct's reference to the Ruby object wrapper. Previously, with GC compaction enabled, a segmentation fault was possible after compaction was triggered. [#2658] (Thanks, @​eightbitraptor and @​peterzhu2118!)
    • [CRuby] Document#remove_namespaces! now defers freeing the underlying xmlNs struct until the Document is GCed. Previously, maintaining a reference to a Namespace object that was removed in this way could lead to a segfault. [#2658]

    sha256 checksums:

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    1.13.8 / 2022-07-23

    Deprecated

    • XML::Reader#attribute_nodes is deprecated due to incompatibility between libxml2's xmlReader memory semantics and Ruby's garbage collector. Although this method continues to exist for backwards compatibility, it is unsafe to call and may segfault. This method will be removed in a future version of Nokogiri, and callers should use #attribute_hash instead. [#2598]

    Improvements

    • XML::Reader#attribute_hash is a new method to safely retrieve the attributes of a node from XML::Reader. [#2598, #2599]

    Fixed

    ... (truncated)

    Changelog

    Sourced from nokogiri's changelog.

    1.13.9 / 2022-10-18

    Security

    Dependencies

    • [CRuby] Vendored libxml2 is updated to v2.10.3 from v2.9.14.
    • [CRuby] Vendored libxslt is updated to v1.1.37 from v1.1.35.
    • [CRuby] Vendored zlib is updated from 1.2.12 to 1.2.13. (See LICENSE-DEPENDENCIES.md for details on which packages redistribute this library.)

    Fixed

    • [CRuby] Nokogiri::XML::Namespace objects, when compacted, update their internal struct's reference to the Ruby object wrapper. Previously, with GC compaction enabled, a segmentation fault was possible after compaction was triggered. [#2658] (Thanks, @​eightbitraptor and @​peterzhu2118!)
    • [CRuby] Document#remove_namespaces! now defers freeing the underlying xmlNs struct until the Document is GCed. Previously, maintaining a reference to a Namespace object that was removed in this way could lead to a segfault. [#2658]

    1.13.8 / 2022-07-23

    Deprecated

    • XML::Reader#attribute_nodes is deprecated due to incompatibility between libxml2's xmlReader memory semantics and Ruby's garbage collector. Although this method continues to exist for backwards compatibility, it is unsafe to call and may segfault. This method will be removed in a future version of Nokogiri, and callers should use #attribute_hash instead. [#2598]

    Improvements

    • XML::Reader#attribute_hash is a new method to safely retrieve the attributes of a node from XML::Reader. [#2598, #2599]

    Fixed

    • [CRuby] Calling XML::Reader#attributes is now safe to call. In Nokogiri <= 1.13.7 this method may segfault. [#2598, #2599]

    1.13.7 / 2022-07-12

    Fixed

    XML::Node objects, when compacted, update their internal struct's reference to the Ruby object wrapper. Previously, with GC compaction enabled, a segmentation fault was possible after compaction was triggered. [#2578] (Thanks, @​eightbitraptor!)

    Commits
    • 897759c version bump to v1.13.9
    • aeb1ac3 doc: update CHANGELOG
    • c663e49 Merge pull request #2671 from sparklemotion/flavorjones-update-zlib-1.2.13_v1...
    • 212e07d ext: hack to cross-compile zlib v1.2.13 on darwin
    • 76dbc8c dep: update zlib to v1.2.13
    • 24e3a9c doc: update CHANGELOG
    • 4db3b4d Merge pull request #2668 from sparklemotion/flavorjones-namespace-scopes-comp...
    • 73d73d6 fix: Document#remove_namespaces! use-after-free bug
    • 5f58b34 fix: namespace nodes behave properly when compacted
    • b08a858 test: repro namespace_scopes compaction issue
    • Additional commits viewable in compare view

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  • Bump tzinfo from 1.2.9 to 1.2.10 in /docs

    Bump tzinfo from 1.2.9 to 1.2.10 in /docs

    Bumps tzinfo from 1.2.9 to 1.2.10.

    Release notes

    Sourced from tzinfo's releases.

    v1.2.10

    TZInfo v1.2.10 on RubyGems.org

    Changelog

    Sourced from tzinfo's changelog.

    Version 1.2.10 - 19-Jul-2022

    Commits
    • 0814dcd Fix the release date.
    • fd05e2a Preparing v1.2.10.
    • b98c32e Merge branch 'fix-directory-traversal-1.2' into 1.2
    • ac3ee68 Remove unnecessary escaping of + within regex character classes.
    • 9d49bf9 Fix relative path loading tests.
    • 394c381 Remove private_constant for consistency and compatibility.
    • 5e9f990 Exclude Arch Linux's SECURITY file from the time zone index.
    • 17fc9e1 Workaround for 'Permission denied - NUL' errors with JRuby on Windows.
    • 6bd7a51 Update copyright years.
    • 9905ca9 Fix directory traversal in Timezone.get when using Ruby data source
    • Additional commits viewable in compare view

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This solves the autonomous driving issue which is supported by deep learning technology. Given a video, it splits into images and predicts the angle of turning for each frame.

Self Driving Car An autonomous car (also known as a driverless car, self-driving car, and robotic car) is a vehicle that is capable of sensing its env

Sagor Saha 4 Sep 4, 2021
DeOldify - A Deep Learning based project for colorizing and restoring old images (and video!)

DeOldify - A Deep Learning based project for colorizing and restoring old images (and video!)

Jason Antic 15.8k Jan 4, 2023
Harmonious Textual Layout Generation over Natural Images via Deep Aesthetics Learning

Harmonious Textual Layout Generation over Natural Images via Deep Aesthetics Learning Code for the paper Harmonious Textual Layout Generation over Nat

null 7 Aug 9, 2022
YOLTv5 rapidly detects objects in arbitrarily large aerial or satellite images that far exceed the ~600×600 pixel size typically ingested by deep learning object detection frameworks

YOLTv5 rapidly detects objects in arbitrarily large aerial or satellite images that far exceed the ~600×600 pixel size typically ingested by deep learning object detection frameworks.

Adam Van Etten 145 Jan 1, 2023