A framework for GPU based high-performance medical image processing and visualization

Overview

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FAST is an open-source cross-platform framework with the main goal of making it easier to do high-performance processing and visualization of medical images on heterogeneous systems utilizing both multi-core CPUs and GPUs. To achieve this, FAST use modern C++, OpenCL and OpenGL.

Get started

See installation instructions for Windows, Ubuntu Linux and macOS.

To start using the framework, check out the C++ Intro Tutorial or the Python Intro Tutorial.

Learn best by example? Check out all the examples for C++ and Python.

For more examples and documentation, go to fast.eriksmistad.no.

Need help? Use the gitter chat: Join the chat on Gitter

Main features

  • Data streaming – Processing pipelines in FAST can handle both static and dynamic/temporal data without any change to the code. FAST can stream data from movie files, your webcamera, an Intel RealSense camera, a sequence of images and even directly from ultrasound scanners such as Clarius.
  • Deep learning – FAST provides a common interface for neural networks supporting different model formats (ONNX, protobuf, SavedModel, OpenVINO, UFF) and backends (Google TensorFlow, NVIDIA TensorRT, Intel OpenVINO), making it possible to create real-time neural network pipelines.
  • High-level data management – Data objects in FAST represent data, such as an image, on all processors. FAST keeps data coherent across the different storage areas thereby removing the burden of explicit memory handling from the developer.
  • Wide data format support - FAST supports several data formats (DICOM, metaimage (MHD), regular jpg/png/bmp images, videos, HDF5, VTK polydata, whole slide images, ultrasound file format) and data types (images 2D and 3D, grayscale and color, image pyramids, surface mesh, vertices, lines, text ++).
  • High performance algorithms – FAST has several high performance parallel OpenCL implementations of common algorithms, such as marching cubes surface extraction, Gaussian smoothing, non-local means, block matching tracking and seeded region growing.
  • Fast concurrent visualization – Rendering and computation are done in separate threads to ensure smooth responsive visualizations. Several types of visualizations are supported both 3D (mesh, point, line, image slice and volume rendering) and 2D (2D image, image slice and segmentation/label rendering, whole slide image (WSI) pyramids).
  • Interoperability – FAST can be used with Python and can also be easily integrated into existing Qt applications.

Research

FAST has been described in the following research articles. If you use this framework for research please cite them:

FAST: framework for heterogeneous medical image computing and visualization Erik Smistad, Mohammadmehdi Bozorgi, Frank Lindseth International Journal of Computer Assisted Radiology and Surgery 2015

High Performance Neural Network Inference, Streaming, and Visualization of Medical Images Using FAST Erik Smistad, Andreas Østvik, André Pedersen IEEE Access 2019

Build

To setup and build the framework, see the instructions for your operating system:

License

FAST itself is licenced under the permissive BSD 2-clause license, however the binary releases of FAST include several third-party libraries which use a number of different open source licences (MIT, Apache 2.0, LGPL ++), see the licences folder in the release for more details.

Surface mesh extracted from a large abdominal CT scan. Alpha blending ray casting rendering of a thorax CT image.

Ultrasound image segmentation using neural netwoks. Whole slide microscopy image.

Comments
  • AirwaySegmentation Test Crashes with Access Violation

    AirwaySegmentation Test Crashes with Access Violation

    Every time I run the airway segmentation test the test fails because of an access violation. Below is the last part of the output before it crashes. Looks like it gets up to the point where it's checking the local convention for the decimal point and then the surface extraction?

    My computer has 2 NVIDIA cards: a Tesla C2075 and a Quaddrro K2200.

    image

    bug 
    opened by DeveloperPaul123 9
  • Segmentation fault when trying to run a Qt executable

    Segmentation fault when trying to run a Qt executable

    For instance, when executing the viewer, I get the following message on the console:

    INFO: Creating new QApp
    INFO: Loaded configuration file: /Users/norman/workspace/dev/misc/fast-medimg/fast-git/build/fast_configuration.txt
    INFO: Test data path: /Users/norman/workspace/dev/misc/fast-medimg/fast-git/data//
    INFO: Kernel source path: /Users/norman/workspace/dev/misc/fast-medimg/fast-git/source/FAST/
    INFO: Kernel binary path: /Users/norman/workspace/dev/misc/fast-medimg/fast-git/build/kernel_binaries/
    INFO: Documentation path: /Users/norman/workspace/dev/misc/fast-medimg/fast-git/doc/
    INFO: Pipeline path: /Users/norman/workspace/dev/misc/fast-medimg/fast-git/pipelines/
    INFO: Creating new GL context for computation thread
    INFO: QApp already exists..
    INFO: Device manager initialize..
    INFO: Initial GL context: 0x7fb83b84dc00
    INFO: Initial GL share group: 0x7fb83cf00000
    INFO: Found 1 OpenCL platforms.
    INFO: 1 platforms selected for inspection.
    INFO: Platform 0: Apple
    INFO: This platform has 0 available devices in total
    INFO: Looking for GPU devices only.
    INFO: 1 selected.
    INFO: Inspecting device 0 with the name Iris Pro
    INFO: Initial GL context: 0x7fb83b84dc00
    INFO: Initial GL share group: 0x7fb83cf00000
    INFO: Device has OpenGL interop capability
    INFO: The device was accepted.
    INFO: A device-platform mismatch was detected.
    INFO: The platform Apple was selected as the best platform.
    INFO: A total of 1 devices were selected for the context from this platform:
    /Users/norman/workspace/dev/misc/fast-medimg/fast-git/pipelines/cardiac_view_classification.fpl
    /Users/norman/workspace/dev/misc/fast-medimg/fast-git/pipelines/dnn_left_ventricle_segmentation.fpl
    /Users/norman/workspace/dev/misc/fast-medimg/fast-git/pipelines/image_renderer.fpl
    asd
    Finished viewer setup
    [1]    59485 segmentation fault  ./bin/viewer
    

    The crash report looks the following. Any ideas? All apps seem to crash for fast::View::initializeGL()

    Process:               viewer [59485]
    Path:                  /Users/USER/*/viewer
    Identifier:            viewer
    Version:               0
    Code Type:             X86-64 (Native)
    Parent Process:        zsh [59397]
    Responsible:           iTerm2 [364]
    User ID:               501
    
    Date/Time:             2017-11-08 16:45:25.050 +0100
    OS Version:            Mac OS X 10.11.6 (15G1510)
    Report Version:        11
    Anonymous UUID:        D1CF50F7-2886-8A51-8DAD-F32E4E57B5F6
    
    Sleep/Wake UUID:       57489E58-DB26-4643-A2A7-1AA259E59714
    
    Time Awake Since Boot: 520000 seconds
    Time Since Wake:       6100 seconds
    
    System Integrity Protection: enabled
    
    Crashed Thread:        0  Dispatch queue: com.apple.main-thread
    
    Exception Type:        EXC_BAD_ACCESS (SIGSEGV)
    Exception Codes:       KERN_INVALID_ADDRESS at 0x0000000000000090
    
    VM Regions Near 0x90:
    --> 
        __TEXT                 0000000107e4a000-0000000107e5a000 [   64K] r-x/rwx SM=COW  /Users/USER/*
    
    Thread 0 Crashed:: Dispatch queue: com.apple.main-thread
    0   libFAST.dylib                 	0x0000000107fc6450 fast::View::initializeGL() + 688
    1   org.qt-project.QtOpenGL       	0x000000010811b1c6 QGLWidget::glInit() + 54
    2   org.qt-project.QtOpenGL       	0x000000010811add5 QGLWidget::resizeEvent(QResizeEvent*) + 85
    3   org.qt-project.QtWidgets      	0x00000001081caec1 QWidget::event(QEvent*) + 2193
    4   org.qt-project.QtWidgets      	0x000000010818aedd QApplicationPrivate::notify_helper(QObject*, QEvent*) + 269
    5   org.qt-project.QtWidgets      	0x000000010818d822 QApplication::notify(QObject*, QEvent*) + 5906
    6   libFAST.dylib                 	0x0000000107fc0dc5 fast::FASTApplication::notify(QObject*, QEvent*) + 21
    7   org.qt-project.QtCore         	0x0000000108ff2aa4 QCoreApplication::notifyInternal2(QObject*, QEvent*) + 164
    8   org.qt-project.QtWidgets      	0x00000001081c39a3 QWidgetPrivate::sendPendingMoveAndResizeEvents(bool, bool) + 275
    9   org.qt-project.QtWidgets      	0x00000001081c964a QWidgetPrivate::show_helper() + 42
    10  org.qt-project.QtWidgets      	0x00000001081ca3bc QWidget::setVisible(bool) + 956
    11  org.qt-project.QtWidgets      	0x00000001081c99b3 QWidgetPrivate::showChildren(bool) + 275
    12  org.qt-project.QtWidgets      	0x00000001081c966c QWidgetPrivate::show_helper() + 76
    13  org.qt-project.QtWidgets      	0x00000001081ca3bc QWidget::setVisible(bool) + 956
    14  libFAST.dylib                 	0x0000000107fc0019 fast::Window::start() + 169
    15  viewer                        	0x0000000107e4e199 main + 41
    16  libdyld.dylib                 	0x00007fff9454a5ad start + 1
    
    Thread 1:
    0   libsystem_kernel.dylib        	0x00007fff95e875e2 __workq_kernreturn + 10
    1   libsystem_pthread.dylib       	0x00007fff97332578 _pthread_wqthread + 1283
    2   libsystem_pthread.dylib       	0x00007fff97330341 start_wqthread + 13
    
    Thread 2:: Dispatch queue: com.apple.libdispatch-manager
    0   libsystem_kernel.dylib        	0x00007fff95e87efa kevent_qos + 10
    1   libdispatch.dylib             	0x00007fff8bbeb165 _dispatch_mgr_invoke + 216
    2   libdispatch.dylib             	0x00007fff8bbeadcd _dispatch_mgr_thread + 52
    
    Thread 3:
    0   libsystem_kernel.dylib        	0x00007fff95e875e2 __workq_kernreturn + 10
    1   libsystem_pthread.dylib       	0x00007fff97332578 _pthread_wqthread + 1283
    2   libsystem_pthread.dylib       	0x00007fff97330341 start_wqthread + 13
    
    Thread 4:
    0   libsystem_kernel.dylib        	0x00007fff95e875e2 __workq_kernreturn + 10
    1   libsystem_pthread.dylib       	0x00007fff97332578 _pthread_wqthread + 1283
    2   libsystem_pthread.dylib       	0x00007fff97330341 start_wqthread + 13
    
    Thread 5:
    0   libsystem_kernel.dylib        	0x00007fff95e875e2 __workq_kernreturn + 10
    1   libsystem_pthread.dylib       	0x00007fff97332578 _pthread_wqthread + 1283
    2   libsystem_pthread.dylib       	0x00007fff97330341 start_wqthread + 13
    
    Thread 6:
    0   libsystem_kernel.dylib        	0x00007fff95e875e2 __workq_kernreturn + 10
    1   libsystem_pthread.dylib       	0x00007fff97332578 _pthread_wqthread + 1283
    2   libsystem_pthread.dylib       	0x00007fff97330341 start_wqthread + 13
    
    Thread 7:
    0   libsystem_kernel.dylib        	0x00007fff95e875e2 __workq_kernreturn + 10
    1   libsystem_pthread.dylib       	0x00007fff97332578 _pthread_wqthread + 1283
    2   libsystem_pthread.dylib       	0x00007fff97330341 start_wqthread + 13
    
    Thread 8:
    0   libsystem_kernel.dylib        	0x00007fff95e875e2 __workq_kernreturn + 10
    1   libsystem_pthread.dylib       	0x00007fff97332578 _pthread_wqthread + 1283
    2   libsystem_pthread.dylib       	0x00007fff97330341 start_wqthread + 13
    
    Thread 9:
    0   libsystem_kernel.dylib        	0x00007fff95e875e2 __workq_kernreturn + 10
    1   libsystem_pthread.dylib       	0x00007fff97332578 _pthread_wqthread + 1283
    2   libsystem_pthread.dylib       	0x00007fff97330341 start_wqthread + 13
    
    Thread 10:
    
    Thread 0 crashed with X86 Thread State (64-bit):
      rax: 0x0000000000000000  rbx: 0x00007fb83b853508  rcx: 0x0000000000000002  rdx: 0x0000000000000000
      rdi: 0x0000000000000001  rsi: 0x00007fb83b853508  rbp: 0x00007fff57db4a40  rsp: 0x00007fff57db4130
       r8: 0x0000000000000006   r9: 0x00000000ffffff00  r10: 0x0000000108d2a401  r11: 0x0000000108b9e750
      r12: 0x00007fb83b853200  r13: 0x9c00cd0040e7b4e7  r14: 0x00007fb83ad3eae0  r15: 0x00007fb83afce410
      rip: 0x0000000107fc6450  rfl: 0x0000000000010206  cr2: 0x0000000000000090
      
    Logical CPU:     0
    Error Code:      0x00000004
    Trap Number:     14
    
    
    Binary Images:
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        0x7fffa1a13000 -     0x7fffa1a15ff7  libsystem_configuration.dylib (802.40.13) <3DEB7DF9-6804-37E1-BC83-0166882FF0FF> /usr/lib/system/libsystem_configuration.dylib
        0x7fffa1a9c000 -     0x7fffa1aa0fff  libpam.2.dylib (20) <CFCD19BD-87BC-3F2B-BB1C-4C23E8E55F1A> /usr/lib/libpam.2.dylib
        0x7fffa1b77000 -     0x7fffa1b88ff7  libsystem_trace.dylib (201.10.3) <E9311C03-9E61-3B13-AF3F-A64956FFF269> /usr/lib/system/libsystem_trace.dylib
        0x7fffa1b89000 -     0x7fffa1e7efff  com.apple.HIToolbox (2.1.1 - 807.2) <36413C45-36AF-34EF-9C0E-F18B31D1E565> /System/Library/Frameworks/Carbon.framework/Versions/A/Frameworks/HIToolbox.framework/Versions/A/HIToolbox
    
    External Modification Summary:
      Calls made by other processes targeting this process:
        task_for_pid: 1
        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: 20166361
        thread_create: 0
        thread_set_state: 0
    
    VM Region Summary:
    ReadOnly portion of Libraries: Total=253.2M resident=0K(0%) swapped_out_or_unallocated=253.2M(100%)
    Writable regions: Total=188.0M written=0K(0%) resident=0K(0%) swapped_out=0K(0%) unallocated=188.0M(100%)
     
                                    VIRTUAL   REGION 
    REGION TYPE                        SIZE    COUNT (non-coalesced) 
    ===========                     =======  ======= 
    Accelerate.framework               128K        2 
    Activity Tracing                  2048K        2 
    CG backing stores                 1328K        5 
    CG image                           296K        6 
    CG shared images                   464K       13 
    CoreAnimation                       40K        7 
    CoreUI image data                  376K        4 
    CoreUI image file                  192K        4 
    Dispatch continuations            16.0M        2 
    IOKit                             14.5M       21 
    Kernel Alloc Once                    8K        3 
    MALLOC                           124.0M       31 
    MALLOC guard page                   32K        7 
    OpenCL                               8K        2 
    STACK GUARD                       56.0M       11 
    Stack                             12.1M       11 
    Stack (reserved)                   520K        2         reserved VM address space (unallocated)
    Stack Guard                          4K        2 
    VM_ALLOCATE                        304K        8 
    __DATA                            18.8M      238 
    __GLSLBUILTINS                    2588K        2 
    __IMAGE                            528K        2 
    __LINKEDIT                        98.5M       33 
    __TEXT                           154.8M      241 
    __UNICODE                          552K        2 
    mapped file                      984.2M      123 
    shared memory                     16.8M       13 
    ===========                     =======  ======= 
    TOTAL                              1.5G      770 
    TOTAL, minus reserved VM space     1.5G      770 
    
    Model: MacBookPro11,4, BootROM MBP114.0172.B16, 4 processors, Intel Core i7, 2.8 GHz, 16 GB, SMC 2.29f24
    Graphics: Intel Iris Pro, Intel Iris Pro, Built-In
    Memory Module: BANK 0/DIMM0, 8 GB, DDR3, 1600 MHz, 0x802C, 0x31364B544631473634485A2D314736453120
    Memory Module: BANK 1/DIMM0, 8 GB, DDR3, 1600 MHz, 0x802C, 0x31364B544631473634485A2D314736453120
    AirPort: spairport_wireless_card_type_airport_extreme (0x14E4, 0x152), Broadcom BCM43xx 1.0 (7.21.95.175.1a6)
    Bluetooth: Version 4.4.6f1 17910, 3 services, 27 devices, 1 incoming serial ports
    Network Service: Thunderbolt Ethernet, Ethernet, en5
    PCI Card: Apple 57762-A0, Ethernet Controller, Thunderbolt@195,0,0
    Serial ATA Device: APPLE SSD SM1024G, 1 TB
    USB Device: USB 3.0 Bus
    USB Device: Apple Internal Keyboard / Trackpad
    USB Device: Bluetooth USB Host Controller
    Thunderbolt Bus: MacBook Pro, Apple Inc., 27.1
    Thunderbolt Device: Thunderbolt to Gigabit Ethernet Adapter, Apple Inc., 3, 5.5
    
    opened by normanius 8
  • Default Directories

    Default Directories

    In CMake, if you don't set the FAST_TEST_DATA_DIR, by default the data is downloaded to the source directory under data/.

    By default, the configuration file doesn't have a data directory set because the FAST_TEST_DATA_DIR variable is empty:

    # from CMakeList.txt, line 170
    # If test data dir has been set by user, write it to config file
    if(NOT FAST_TEST_DATA_DIR STREQUAL "")
        # Remove any existing "
        string(REPLACE "\"" "" FAST_TEST_DATA_DIR ${FAST_TEST_DATA_DIR})
        message("-- FAST test data directory set to: \"${FAST_TEST_DATA_DIR}\"")
        set(CONFIG_TEST_DATA_PATH "TestDataPath = ${FAST_TEST_DATA_DIR}/")
    endif()
    

    When reading the configuration file during the tests, the data path defaults to a directory named data in the build directory.

    The same is true of the KernelSourcePath (see lines 71-80 of Config.cpp).

    void loadConfiguration() {
        if (mConfigurationLoaded)
        	    return;
    
        // Set default paths
        mTestDataPath = getPath() + "../data/";
        mKernelSourcePath = getPath() + "../source/FAST/";
        mKernelBinaryPath = getPath() + "kernel_binaries/";
        mDocumentationPath = getPath() + "../doc/";
        mPipelinePath = getPath() + "../pipelines/";
        //....
    }
    

    The simple fix is to set the KernelSourcePath to a default value and to set the FAST_TEST_DATA_DIR variable to the default value if the user doesn't set it explicitly.

    opened by DeveloperPaul123 5
  • PW classifier predictions are off-by-one when renderering

    PW classifier predictions are off-by-one when renderering

    Describe the bug When running a simple pw-classification pipeline, using runPipeline, I observed that the predictions were off-by-one. Both horizontally and vertically.

    To Reproduce The FPL file and corresponding model and WSI can be downloaded from this URL: https://drive.google.com/drive/folders/1jeovs5hco5U2wPrTHXiWghwiaugP6F6L?usp=sharing

    Simply update the paths and run using runPipeline.

    To make it easier to debug, I would disable heatmap interpolation (that is useInterpolation = false in FP). I have not started doing heatmap stuff with runPipeline yet in FP by default, and therefore not noticed that this was not possible to set this using an Attribute with Pipelines. Simply add option for Attribute here (setInterpolation method already exists): https://github.com/smistad/FAST/blob/e47eb8bb75dbb5f4a9983471d261edd09b5250cf/source/FAST/Visualization/HeatmapRenderer/HeatmapRenderer.cpp#L16

    Otherwise I can make a PR for that.

    System:

    • Bug found using Windows 10
    • Both NVIDIA and Intel OpenCL vendors were tested and produced the same bug

    Expected behavior One-to-one correspondence between WSI and heatmap, aligned in a way that makes sense.

    Screenshots example-off-by-one

    Disclaimer The newest commit of FAST was not used for doing the tests, but rather an older release (probably a month old). If it works for you I can try updating FAST and redoing the tests.

    bug 
    opened by andreped 4
  • Bug fix regarding import/export not being executed

    Bug fix regarding import/export not being executed

    1. Added fix to ensure that import/export of low and high-res images are being properly exported in C++.
    2. Added small fix for RGB/BGR switch in ImageExporter. 3. Added option to set attributes through text pipelines for a selection of classes. 4. Added method to set the tissue acceptance threshold (which was hard-coded to 0.5). 5. Lowered the image pyramidal plane size threshold to accept lower image planes in the stitcher, which were relevant in a study. I left a comment in the code if we want to switch back and compare in the future. 6. Also did some minor refactoring to keep the code more consistent on a selection of files.

    Tested this using FastPathology on Ubuntu, both through C++ and runPipeline. Both worked fine.

    (1) The bug was found comparing the specific classes against HDF5TensorExporter/HDF5TensorImporter, which seemed to work great. Using these two scripts as reference I noticed some minor differences.

    I found that adding setModified(true) was quite important. I'm not really sure why not just using mIsModified=true did not work. Perhaps there was something I forgot to add to the header. Now there are occasions where I am using both setModified(true) AND mIsModified=true, where the latter is then probably redundant? Perhaps it is easier for you to say what is correct, so I just left it like this for you to evaluate.

    I am also assuming that there should be an override for the execute method if the parent class has an execute method already. I noticed that this was done for the HDF5TensorExporter, and not the specific scripts I was working with. However, in this case it did not really seem to matter.

    (2) I noticed that when creating thumbnail images from WSIs, that the exported images had swapped RGB channels. I proposed a simple fix that I have used earlier, but perhaps there is a more elegant solution. This should reproduce the bug: auto importer = WholeSlideImageImporter::New(); importer->setFilename(path_to_wsi); auto currImage = importer->updateAndGetOutputData();

    auto access = currImage->getAccess(ACCESS_READ); auto input = access->getLevelAsImage(currImage->getNrOfLevels() - 1)

    auto exporter = ImageExporter::New(); exporter->setFilename(path_to_output.png); exporter->setInputData(input); exporter->update();

    (5) Perhaps this threshold should even be a hyperparameter one could set? But for now, I just manually lowered them. Did not notice any real difference using runPipeline and FastPathology on Ubuntu, for A05.svs and a larger 40x WSI from a local cohort. But perhaps overall runtime of high-res models will be slower? Right? But perhaps this is okay for now?

    Let me know if there are any requests.

    opened by andreped 4
  • OpenVINO encrypted models

    OpenVINO encrypted models

    could this part "2. Read IR Generated by ModelOptimizer (.xml and .bin files)" be put into a new virtual function just for loading model and weights from memory buffers?

    virtual void readNetwork(CNNNetReader network_reader);

    default implementation could be something like void OpenVINOEngine::readNetwork(CNNNetReader network_reader) { auto input_model = getFilename(); if(!fileExists(input_model)) throw FileNotFoundException(input_model); network_reader.ReadNetwork(fileNameToString(input_model)); network_reader.ReadWeights(fileNameToString(input_model).substr(0, input_model.size() - 4) + ".bin"); }

    https://docs.openvinotoolkit.org/latest/_docs_IE_DG_protecting_model_guide.html

    new feature 
    opened by rolarsen 4
  • build error in window

    build error in window

    Hello: when I build in win,there is an error error C2664 “void std::_Ptr_base<_Ty>::_Reset0(_Ty (*),std::_Ref_count_base *)”: can not change “uchar ”to“unsigned char ()[]” error occured in FAST memory.cpp line 695

    opened by huangsusan 4
  • No SOVERSION

    No SOVERSION

    Hello there,

    I have realised that libFAST.so contains no SOVERSION. This can be set within CMAKE itself to be set during build time. The SOVERSION can then be incremented / bumped whenever there is a change with ABI. Could start with a SOVERSION of 0. Would it be possible if you could add this?

    The SOVERSION is important within packaging and the debian ecosystem with regards to ABI backwards compat and a plethora of other things.

    Thanks, Shayan

    opened by shayandoust 4
  • Build error on VTKImageExporter

    Build error on VTKImageExporter

    I tried adding update(0) in VTKImageExporter.cpp and VTKImageImporter.cpp, which fixed one error but there was another error I couldn't figure out.

    10%] Building CXX object CMakeFiles/FAST.dir/source/FAST/Exporters/VTKImageExporter.cpp.o
    [ 11%] Building CXX object CMakeFiles/FAST.dir/source/FAST/Exporters/VTKMeshExporter.cpp.o
    /build/fast/FAST/source/FAST/Exporters/VTKImageExporter.cpp: In member function ‘virtual int fast::VTKImageExporter::RequestData(vtkInformation*, vtkInformationVector**, vtkInformationVector*)’:
    /build/fast/FAST/source/FAST/Exporters/VTKImageExporter.cpp:53:12: error: no matching function for call to ‘fast::VTKImageExporter::update()’
         update(); // Run FAST pipeline
                ^
    In file included from /build/fast/FAST/source/FAST/Exporters/VTKImageExporter.hpp:5:0,
                     from /build/fast/FAST/source/FAST/Exporters/VTKImageExporter.cpp:1:
    /build/fast/FAST/source/FAST/ProcessObject.hpp:28:14: note: candidate: void fast::ProcessObject::update(uint64_t, fast::StreamingMode)
             void update(uint64_t timestep, StreamingMode streamingMode = STREAMING_MODE_PROCESS_ALL_FRAMES);
                  ^
    /build/fast/FAST/source/FAST/ProcessObject.hpp:28:14: note:   candidate expects 2 arguments, 0 provided
    /build/fast/FAST/source/FAST/Exporters/VTKMeshExporter.cpp: In member function ‘virtual int fast::VTKMeshExporter::RequestData(vtkInformation*, vtkInformationVector**, vtkInformationVector*)’:
    /build/fast/FAST/source/FAST/Exporters/VTKMeshExporter.cpp:30:12: error: no matching function for call to ‘fast::VTKMeshExporter::update()’
         update(); // Run FAST pipeline
                ^
    In file included from /build/fast/FAST/source/FAST/Exporters/VTKMeshExporter.hpp:5:0,
                     from /build/fast/FAST/source/FAST/Exporters/VTKMeshExporter.cpp:1:
    /build/fast/FAST/source/FAST/ProcessObject.hpp:28:14: note: candidate: void fast::ProcessObject::update(uint64_t, fast::StreamingMode)
             void update(uint64_t timestep, StreamingMode streamingMode = STREAMING_MODE_PROCESS_ALL_FRAMES);
                  ^
    /build/fast/FAST/source/FAST/ProcessObject.hpp:28:14: note:   candidate expects 2 arguments, 0 provided
    CMakeFiles/FAST.dir/build.make:1048: recipe for target 'CMakeFiles/FAST.dir/source/FAST/Exporters/VTKImageExporter.cpp.o' failed
    make[2]: *** [CMakeFiles/FAST.dir/source/FAST/Exporters/VTKImageExporter.cpp.o] Error 1
    make[2]: *** Waiting for unfinished jobs....
    CMakeFiles/FAST.dir/build.make:1061: recipe for target 'CMakeFiles/FAST.dir/source/FAST/Exporters/VTKMeshExporter.cpp.o' failed
    make[2]: *** [CMakeFiles/FAST.dir/source/FAST/Exporters/VTKMeshExporter.cpp.o] Error 1
    CMakeFiles/Makefile2:69: recipe for target 'CMakeFiles/FAST.dir/all' failed
    make[1]: *** [CMakeFiles/FAST.dir/all] Error 2
    Makefile:129: recipe for target 'all' failed
    make: *** [all] Error 2
    
    

    Couldn't figure this one out

    [ 62%] Building CXX object CMakeFiles/testFAST.dir/source/FAST/Importers/Tests/VTKImageImporterTests.cpp.o
    In file included from /build/fast/FAST/source/FAST/Importers/VTKImageImporter.hpp:4:0,
                     from /build/fast/FAST/source/FAST/Importers/Tests/VTKImageImporterTests.cpp:2:
    /build/fast/FAST/source/FAST/ProcessObject.hpp: In function ‘void ____C_A_T_C_H____T_E_S_T____10()’:
    /build/fast/FAST/source/FAST/ProcessObject.hpp:170:21: error: ‘fast::DataObject::pointer fast::ProcessObject::getOutputData(uint) [with DataType = fast::Image; fast::DataObject::pointer = fast::SharedPointer<fast::DataObject>; uint = unsigned int]’ is protected
     DataObject::pointer ProcessObject::getOutputData(uint portID) {
                         ^
    /build/fast/FAST/source/FAST/Importers/Tests/VTKImageImporterTests.cpp:13:63: error: within this context
         Image::pointer fastImage = importer->getOutputData<Image>();
                                                                   ^
    In file included from /build/fast/FAST/source/FAST/Importers/VTKImageImporter.hpp:4:0,
                     from /build/fast/FAST/source/FAST/Importers/Tests/VTKImageImporterTests.cpp:2:
    /build/fast/FAST/source/FAST/ProcessObject.hpp:170:21: error: ‘fast::DataObject::pointer fast::ProcessObject::getOutputData(uint) [with DataType = fast::Image; fast::DataObject::pointer = fast::SharedPointer<fast::DataObject>; uint = unsigned int]’ is protected
     DataObject::pointer ProcessObject::getOutputData(uint portID) {
                         ^
    /build/fast/FAST/source/FAST/Importers/Tests/VTKImageImporterTests.cpp:23:70: error: within this context
         Image::pointer importedImage = vtkImporter->getOutputData<Image>();
                                                                          ^
    CMakeFiles/testFAST.dir/build.make:400: recipe for target 'CMakeFiles/testFAST.dir/source/FAST/Importers/Tests/VTKImageImporterTests.cpp.o' failed
    make[2]: *** [CMakeFiles/testFAST.dir/source/FAST/Importers/Tests/VTKImageImporterTests.cpp.o] Error 1
    CMakeFiles/Makefile2:180: recipe for target 'CMakeFiles/testFAST.dir/all' failed
    make[1]: *** [CMakeFiles/testFAST.dir/all] Error 2
    Makefile:129: recipe for target 'all' failed
    make: *** [all] Error 2
    
    
    opened by kayarre 4
  • Small Enhancement

    Small Enhancement

    Changed from for loop to a stl algorithm to find min and max of the input coordinates of a bounding box. This may also result in a small optimization.

    I should also note that I tested this by running the BoundingBoxRendererTests and it passed.

    opened by DeveloperPaul123 4
  • GVF example error

    GVF example error

    Describe the bug

    My code is :

    #include "FAST/Importers/ImageFileImporter.hpp" #include "FAST/Visualization/ImageRenderer/ImageRenderer.hpp" #include "FAST/Visualization/SegmentationRenderer/SegmentationRenderer.hpp" #include "FAST/Visualization/SimpleWindow.hpp" #include "FAST/Algorithms/GradientVectorFlow/EulerGradientVectorFlow.hpp" #include "FAST/Algorithms/GradientVectorFlow/MultigridGradientVectorFlow.hpp"

    using namespace fast;

    int main() { auto importer = ImageFileImporter::create("d:\1.jpg"); auto gvf = MultigridGradientVectorFlow::create()->connect(importer); auto imageRenderer = ImageRenderer::create()->connect(importer); auto segmentationRenderer = SegmentationRenderer::create()->connect(gvf); auto window = SimpleWindow2D::create()->connect({ imageRenderer, segmentationRenderer }); #ifdef FAST_CONTINUOUS_INTEGRATION // This will automatically close the window after 5 seconds, used for CI testing window->setTimeout(5 * 1000); #endif window->run(); return 0; }

    The source code can be compiled, but it will run into errors!

    To Reproduce The source code can be compiled, but it will run into errors.Run will throw an exception.

    virtual bool notify(QObject *receiver, QEvent *event) { try { return QApplication::notify(receiver, event); } catch(Exception &e) { Reporter::error() << "FAST exception caught in Qt event handler " << e.what() << Reporter::end(); throw e;

    System:

    • OS and version: [e.g. Windows 10]
    • GPU and OpenCL vendor (NVIDIA, Intel)

    Expected behavior Can you give an example to correctly use EulerGradientVectorFlow and MultigridGradientVectorFlow?

    Screenshots If applicable, add screenshots to help explain your problem. image

    bug 
    opened by libaineu2004 3
  • Vulnerable shared libraries might make pyFAST vulnerable. Can you help upgrade to patch versions?

    Vulnerable shared libraries might make pyFAST vulnerable. Can you help upgrade to patch versions?

    Hi, @smistad , @andreped , I'd like to report a vulnerability issue in pyfast_4.3.0.

    Dependency Graph between Python and Shared Libraries

    image

    Issue Description

    As shown in the above dependency graph(here shows part of the dependency graph, which depends on vulnerable shared libraries), pyfast_4.3.0 directly or transitively depends on 81 C libraries (.so). However, I noticed that some C libraries are vulnerable, containing the following CVEs: libdcmdata.so.13, libdcmimgle.so.13, liboflog.so.13 and libofstd.so.13from C project dcmtk(version:3.6.2) exposed 1 vulnerabilities: CVE-2019-1010228 libgdk_pixbuf-2.0.so.0 from C project gdk-pixbuf(version:2.36.11) exposed 4 vulnerabilities: CVE-2021-20240, CVE-2017-6313, CVE-2017-6312, CVE-2017-6314 libhdf5.so.103 and libhdf5_cpp.so.103 from C project hdf5(version:1.10.6) exposed 4 vulnerabilities: CVE-2020-10811, CVE-2020-10812, CVE-2020-10810, CVE-2020-10809 libicudata.so.60 and libicuuc.so.60 from C project icu(version:60.2) exposed 1 vulnerabilities: CVE-2020-21913 libjpeg.so.8from C project libjpeg-turbo(version:1.5.2) exposed 2 vulnerabilities: CVE-2018-14498, CVE-2017-15232 libopenjp2.so.7 from C project openjpeg2(version:2.3.0) exposed 8 vulnerabilities: CVE-2020-6851, CVE-2020-27824, CVE-2020-27823, CVE-2018-18088, CVE-2018-14423, CVE-2018-6616, CVE-2018-5785, CVE-2017-17480 libxml2.so.2 from C project libxml2(version:2.9.4) exposed 22 vulnerabilities: CVE-2016-4658, CVE-2016-1838, CVE-2021-3541, CVE-2021-3537, CVE-2021-3518, CVE-2021-3517,CVE-2019-19956, CVE-2018-14404, CVE-2017-18258, CVE-2017-5130, CVE-2017-15412, CVE-2017-7375, CVE-2017-7376, CVE-2017-9050, CVE-2017-9047, CVE-2017-9048, CVE-2017-9049, CVE-2017-8872, CVE-2017-5969, CVE-2016-9318, CVE-2017-16931, CVE-2017-16932

    Furthermore, the vulnerable methods in the vulnerable shared libraries can be actually invoked by Python code. For instance, the following call chain can reach the vulnerable method(C code) H5AC_unpin_entry() in file H5AC.c reported by CVE-2020-10810.

    call chain-----
    H5Literate_by_name()->H5G_iterate()->H5G__obj_iterate()->H5G__dense_iterate()->H5HF_open()->H5HF_close()->H5HF_space_close()->H5FS_close()->H5FS_decr()->H5AC_unpin_entry()
    

    Suggested Vulnerability Patch Versions

    dcmtk has fixed the vulnerabilities in versions >=3.6.4 gdk-pixbuf has fixed the vulnerabilities in versions >=2.42.0 hdf5 has fixed the vulnerabilities in versions >=1.12.1 libjpeg-turbo has fixed the vulnerabilities in versions >=2.0.0 icu has fixed the vulnerabilities in versions >=67-1 openjpeg2 has fixed the vulnerabilities in versions >=2.4.0 libxml2 has fixed the vulnerabilities in versions >=2.9.11

    Python build tools cannot report vulnerable C libraries, which may induce potential security issues to many downstream Python projects. As a popular python package (pyfast has 2,048 downloads per month), could you please upgrade the above shared libraries to their patch versions?

    Thanks for your help~ Best regards, MikeWazowski

    bug 
    opened by MikeWazoWski123 1
  • MacOS Running issue

    MacOS Running issue

    Describe the bug I installed FAST on MacOS12.2.1 (21D62) but I can’t run this https://fast.eriksmistad.no/scan_convert_iq_data_8py-example.html example

    Please do not use issues for questions. Use the gitter chat for questions and discussions: https://gitter.im/smistad/FAST

    To Reproduce

    1. install homebrew /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
    2. install required package brew install openslide libomp
    3. In Anaconda pip install pyfast
    4. In pycharm run the code provided in this example import fast import numpy as np import matplotlib.pyplot as plt

    Create some fake IQ beamspace data

    iq_data = fast.Image.createFromArray(np.random.normal(size=(512,512,2)).astype(np.float32))

    Add some geometric data used for scan conversion

    iq_data.setFrameData('isPolar', 'true') iq_data.setFrameData('startRadius', '0') iq_data.setFrameData('stopRadius', '0.12') iq_data.setFrameData('startTheta', '-0.785398') # Start and stop angle in radians iq_data.setFrameData('stopTheta', '0.785398') iq_data.setFrameData('depthSpacing', '0.000235') iq_data.setFrameData('azimuthSpacing', '0.003074')

    Create processing & visualization chain

    envelope = fast.EnvelopeAndLogCompressor.create()
    .connect(iq_data)

    scan_convert = fast.ScanConverter.create()
    .connect(envelope)

    renderer = fast.ImageRenderer.create()
    .connect(scan_convert)

    fast.SimpleWindow2D.create()
    .connect(renderer)
    .run()

    Visualize output of scan converter with matplotlib instead of FAST:

    plt.imshow(np.asarray(scan_convert.runAndGetOutputData())[..., 0], cmap='gray') plt.show()

    System:

    • MacBooks Pro 2019. MacOS12.2.1 (21D62)
    • Radeon Pro Vega 20 4 GB
    • Intel UHD Graphics 630 1536 MB

    Expected behavior fake IQ data will show up Screenshots Error message from pycharm qt.qpa.window: <QNSWindow: 0x7fac7d9f6bb0; contentView=<QNSView: 0x7fac7d9f68f0; QCocoaWindow(0x6000031a5810, window=QWidgetWindow(0x6000022ed380, name="QGLWidgetClassWindow"))>> has active key-value observers (KVO)! These will stop working now that the window is recreated, and will result in exceptions when the observers are removed. Break in QCocoaWindow::recreateWindowIfNeeded to debug.

    bug 
    opened by young-oct 2
  • Concatenation method

    Concatenation method

    There are numerous scenarios where it might be of interest to apply a concatenation, especially of images/segmentations. I found that in the FlowNetwork script there is a PO called ImagesToSequence, which seems to do a concatenation along the "time" axis: https://github.com/smistad/FAST/blob/5b88fcc1d24fdf6f458f0876cd75890c25784e85/source/FAST/Algorithms/NeuralNetwork/FlowNetwork.cpp#L53

    However, there is not an option to set which axis to concatenate along. It should also be easy to concatenate multiple elements simultaneously, similarly as done in numpy: output = np.concatenate([x1, x2, ...], axis=k)

    new feature 
    opened by andreped 0
  • setup for windows debug build

    setup for windows debug build

    https://github.com/smistad/FAST/blob/2a8aa0ba76cd4096db78a8ae482a3effbea96351/source/FAST/Algorithms/NeuralNetwork/InferenceEngines/CMakeLists.txt#L56

    if(WIN32)
        target_link_libraries(InferenceEngineOpenVINO FAST optimized inference_engine.lib debug inference_engined.lib)
    else()
       target_link_libraries(InferenceEngineOpenVINO FAST libinference_engine.so)
    endif()
    

    In addition the cmake/ExternalOpenVINO.cmake must be changed locally to build and install Debug version of OpenVINO. Found no way so far to setup cmake for automatic for this....

        BUILD_COMMAND
            ${CMAKE_COMMAND} --build . --config Debug --target inference_engine COMMAND
            ${CMAKE_COMMAND} --build . --config Debug --target clDNNPlugin COMMAND
            ${CMAKE_COMMAND} --build . --config Debug --target myriadPlugin COMMAND
            ${CMAKE_COMMAND} --build . --config Debug --target MKLDNNPlugin
        INSTALL_COMMAND
            ${CMAKE_COMMAND} -E copy ${FAST_EXTERNAL_BUILD_DIR}/OpenVINO/src/OpenVINO/bin/intel64/Debug/inference_engined.lib ${FAST_EXTERNAL_INSTALL_DIR}/lib/ COMMAND
            ${CMAKE_COMMAND} -E copy ${FAST_EXTERNAL_BUILD_DIR}/OpenVINO/src/OpenVINO/bin/intel64/Debug/inference_engined.dll ${FAST_EXTERNAL_INSTALL_DIR}/bin/ COMMAND
            ${CMAKE_COMMAND} -E copy ${FAST_EXTERNAL_BUILD_DIR}/OpenVINO/src/OpenVINO/bin/intel64/Debug/clDNNPlugind.dll ${FAST_EXTERNAL_INSTALL_DIR}/bin/ COMMAND
            ${CMAKE_COMMAND} -E copy ${FAST_EXTERNAL_BUILD_DIR}/OpenVINO/src/OpenVINO/bin/intel64/Debug/MKLDNNPlugind.dll ${FAST_EXTERNAL_INSTALL_DIR}/bin/ COMMAND
            ${CMAKE_COMMAND} -E copy ${FAST_EXTERNAL_BUILD_DIR}/OpenVINO/src/OpenVINO/bin/intel64/Debug/myriadPlugind.dll ${FAST_EXTERNAL_INSTALL_DIR}/bin/ COMMAND
            ${CMAKE_COMMAND} -E copy ${FAST_EXTERNAL_BUILD_DIR}/OpenVINO/src/OpenVINO/bin/intel64/Debug/ngraphd.dll ${FAST_EXTERNAL_INSTALL_DIR}/bin/ COMMAND
            ${CMAKE_COMMAND} -E copy ${FAST_EXTERNAL_BUILD_DIR}/OpenVINO/src/OpenVINO/bin/intel64/Debug/plugins.xml ${FAST_EXTERNAL_INSTALL_DIR}/bin/ COMMAND
            ${CMAKE_COMMAND} -E copy ${FAST_EXTERNAL_BUILD_DIR}/OpenVINO/src/OpenVINO/inference-engine/temp/tbb/bin/tbb.dll ${FAST_EXTERNAL_INSTALL_DIR}/bin/ COMMAND
            ${CMAKE_COMMAND} -E copy_directory ${FAST_EXTERNAL_BUILD_DIR}/OpenVINO/src/OpenVINO/inference-engine/include/ ${FAST_EXTERNAL_INSTALL_DIR}/include/openvino/
        )
    
    opened by rolarsen 2
  • Us3Dhybrid

    Us3Dhybrid

    Her kommer pull-requesten av oppgave-koden! Er det noe problemer med den kan dere også hente koden på git: rubenfagerli/Fast branch: Us3Dhybrid

    Den kan sikkert være ganske vanskelig å merge direkte, da det er kode hentet fra Mehdis renderer-branch osv.. Trolig lettere å bruke de relevante filene og bygge det rundt dem til nyeste versjon.

    Algoritmefilene ligger i: FAST/Algorithms/UsReconstruction/Us3Dhybrid/ Us3Dhybrid.cpp er algoritme-filen, mens kernelene er splittet opp i flere filer.

    I hovedsak er us3Dhybrid.cl kernel for hybrid-accumulering, både med vanlig compounding og "alpha"-blending. Gaussian og linear er kompilerings-options "USE_GAUSSIAN_WEIGHT".

    us3Dpnn.cl gjør både akkumulering og normalizering (med local hole filling)

    normalizeVolume.cl er normalisering kernel for hybrid.

    Ellers er det så vidt jeg husker overskuddsfiler, brukt i testing etc.

    Eksempelfilen ligger i : FAST/Examples/UltrasoundReconstruction/Us3Dhybrid/ Og heter RunUs3Dhybrid.cpp Denne kan se på for å se hvordan algoritmen kjøres. Main metoden kjører enten en performance test, eller en(/et sett med) rekonstruksjon. Det er den første/øverste void runAlgorithmAndExportImage(...) funksjonen som er gjeldende.

    Filene kan være litt kaotiske og med noen overflødige funksjoner, men bare start fra execute(), constructor og main() metodene så skal det være ganske greit. Og se på eksempelet for å se alle variablene som bør settes. Er det noen andre spørsmål, bare spør. -Ruben

    opened by ruben-fa 0
Releases(v4.6.0)
Owner
Erik Smistad
Research scientist working on medical image processing with focus on image segmentation, ultrasound images, deep learning and GPU processing
Erik Smistad
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