FastPathology

Open-source software for deep learning-based digital pathology

FastPathology was created by researchers at SINTEF and the Norwegian University of Science and Technology (NTNU). A paper presenting the software and some benchmarks has been published in IEEE Access.

💻 Getting started

To install FastPathology, follow the instructions for your operating system:

Windows (10 or newer)

• Download and run the Windows installer from the release page. Note: Windows might prompt you with a security warning, to proceed you must press "More info" followed by "Run anyway".
• Run fastpathology from your start menu.
• To uninstall the application, go to start menu -> remove programs -> find fastpathology and select uninstall. Optionally you can also delete your C:/Users/"your username"/fastpathology/ which includes stored project results, pipelines, and models. And the folder C:/ProgramData/FAST/ which contains a cache.

Ubuntu Linux (18.04 or newer)

• Install OpenCL for Linux by downloading an implementation depending on the CPU/GPU you have:
  • NVIDIA - Install CUDA.
  • Intel - Install the OpenCL NEO driver.
  • AMD - Install the ROCm stack.
  • If none of the above fits, you can try the Portable Computing Lanauge (PCOL), although reduced performance is likely.

• Download the debian package from the release page.
• Install the debian package from the terminal or by double-clicking it:

sudo dpkg -i fastpathology_ubuntu*.deb

• Go to the folder /opt/fastpathology/bin/ and run the fastpathology executable, or run it from the ubuntu menu (windows button->type fastpathology).
• To uninstall the application, run the following in your terminal:

sudo apt remove fastpathology
# Optionally, you can also delete your fastpathology folder 
# which includes stored project results, pipelines and models.
# and the FAST folder which stores cache files.
rm -Rf $HOME/fastpathology
rm -Rf $HOME/FAST

macOS (10.13 or newer)

Note that the macOS version of FastPathology is experimental.

• Install homebrew if you don't already have it. Then, install the following packages using homebrew:

brew install openslide libomp

• Download and run the macOS installer (.dmg) from the release page. Download the x86_64 file if you have an Intel CPU, or download the arm64 file if you have an Apple Silicon (M1/M2) CPU.
• In the installer, drag-and-drop the FastPathology bundle to the Applications directory.
• When the copy is finished, double-click the Applications icon in the installer and right-click + open FastPathology from the Applications menu.
• A warning should be prompted "macOS cannot verify the developer of FastPathology (...)", click open and the program should launch.

Note: This is only required to be done once. For all future usage, launch FastPathology as a regular App bundle.

• To uninstall the application, delete the extracted folder. Optionally, you can also delete the /Users/"your username"/fastpathology/ folder, which includes stored project results, pipelines, and models. And the folder /Users/"your username"/FAST/ which contains a cache.

Optional: NVIDIA GPU Inference

If you have an NVIDIA GPU on your machine you can enable high-speed inference by downloading and installing the following:

• CUDA 11
• cuDNN 8.2
• TensorRT 8.2

Note: Make sure to download the correct versions. NVIDIA GPU inference is not supported on Mac.

License

The source code of FastPathology is licensed under the BSD 2-clause license, however the FastPathology program use and are linked with many great third-party libraries which have several different open source licenses, see the licenses folder in the installation folder for more details.

📹 Demos and tutorials

Very simple demonstrations of the platform can be found on Youtube. More in-depth demonstrations will be added in the future. Wikis and tutorials can be found in the wiki. More information can be found from the pages section on the right in the wiki home.

🎊 Features

The software is implemented in C++ based using FAST. A wide range of features have been added to make working with whole slide images (WSIs) a piece of cake!

• Graphical User Interface - User-friendly GUI for working with WSIs without any code interaction.
• Deep learning - Deployment and support for multi-input/output convolutional neural networks (CNNs).
• Visualization - Real-time streaming of predictions on top of the WSI with low memory cost.
• Use cases - Patch-wise classification, low and high-resolution segmentation, and object detection are supported.
• Inference Engines - FAST includes a variety of different inference engines, i.e. TensorFlow CPU/CUDA (support both TF v1 and v2 models), TensorRT (UFF and ONNX), OpenVINO (CPU/GPU/VPU), and ONNX Runtime (CPU/GPU).
• Text pipelines - Possibility to create your own pipelines using the built-in script editor.
• Formats - Through OpenSlide FastPathology supports various WSI formats.

🔬 Applications of FastPathology

• Pettersen et al., Code-free development and deployment of deep segmentation models for digital pathology (2022), Frontiers in Medicine, https://doi.org/10.3389/fmed.2021.816281
• Pedersen et al., H2G-Net: A multi-resolution refinement approach for segmentation of breast cancer region in gigapixel histopathological images (2022), Frontiers in Medicine, https://doi.org/10.3389/fmed.2022.971873
• Røyset et al., Deep learning-based image analysis reveals significant differences in the number and distribution of mucosal CD3 and γδ T cells between Crohn's disease and ulcerative colitis (2022), The Journal of Pathology, https://doi.org/10.1002/cjp2.301
• Høibø et al., Immunohistochemistry guided segmentation of benign epithelial cells, in situ lesions, and invasive epithelial cells in breast cancer slides (2023), arXiv (preprint), https://doi.org/10.48550/arXiv.2311.13261
• Chiou et al., An immunohistochemical atlas of necroptotic pathway expression (2024), EMBO Molecular Medicine, https://doi.org/10.1038/s44321-024-00074-6

🔨 Development setup

1. Either
   • Download and install a release of FAST.
   • Compile and install FAST on your system.
2. Clone this repository

   git clone https://github.com/AICAN-Research/FAST-Pathology.git

3. Setup build environment using CMake
   Linux (Ubuntu)

   mkdir build
   cd build
   cmake .. -DFAST_DIR=/path/to/FAST/cmake/

   Windows (Visual Studio) Modify generator -G string to match your Visual studio version. This command will create a visual studio solution in your build folder.

   mkdir build
   cd build
   cmake .. -DFAST_DIR=C:\path\to\FAST\cmake\ -G "Visual Studio 16 2019" -A x64

4. Build

   cmake --build . --config Release --target fastpathology

5. Run Linux (Ubuntu)

   ./fastpathology

   Windows

   cd Release
   fastpathology.exe

NOTE: Visual Studio 19 has been tested with both FAST and FastPathology and works well.

✨ How to cite

Please, consider citing our paper, if you find the work useful:

@article{pedersen2021fastpathology,
    author={Pedersen, André and Valla, Marit and Bofin, Anna M. and De Frutos, Javier Pérez and Reinertsen, Ingerid and Smistad, Erik},
    journal={IEEE Access}, 
    title={{FastPathology: An Open-Source Platform for Deep Learning-Based Research and Decision Support in Digital Pathology}}, 
    year={2021},
    volume={9},
    number={},
    pages={58216-58229},
    doi={10.1109/ACCESS.2021.3072231}
}