DISCLAIMER: This is a work in progress. When I have the plugin properly working and stable, I will make a public docker image, and make a release here.
Note that this setup has been tested against Ubuntu 18.04 and 20.04. It should likely work on Windows 10, but on macOS there is a conflict between OpenGL/OpenCL resulting in a RuntimeError: clGetPlatformIDs.
Click watch in the top right if this project interests you and want to be updated when it is ready to be tested.
The software is still in development, but some key features have been added such as:
- Uses pyFAST backend to run FAST pipelines (FPLs)
- Developed generic backend tool for running FPLs through the UI and convert predictions to the JSON format
- Ability to run patch-wise classification and segmentation models
- Render classification predictions as heatmaps and segmentation objects as boundaries
- Store predictions in database, access, download, and modify these through the UI
DSA needs to be installed. Follow the instructions here on how to do so.
In addition, docker need to be setup such that it works with pyFAST. For that I strongly recommend installing Docker desktop. You might also need to install the nvidia docker to make it work properly:
sudo apt update
sudo apt-get install -y nvidia-docker2
sudo systemctl restart docker
Clone the repository:
git clone https://github.com/andreped/FP-dsa-plugin.git
Build the docker image for the plugin:
cd dsa/
docker build -t fastpathology .
To add the plugin to DSA, choose Upload new Task under Slicer CLI Web Tasks in the DSA web UI, and write fastpathology:latest and click Import image. The plugin can then be used from the Analysis Page.
The core was built based on pyFAST, and the plugin was inspired by the plugins made for MONAILabel and HistomicsTK. Conversion of pyFAST's pyramidal TIFF annotations to HistomicsTK's JSON annotations was enabled using OpenCV.
The plugin was made for the Digital Slide Archive which has developed an open and extremely robust and user-friendly web solution for archiving, visualizing, processing, and annotating large microscopy images. Building our methods on top of DSA was done with ease and credit to the developers such as manthey and dgutman for addressing any issue and concerns we had at impressive speed!
The plugin has MIT-License.
Note that the different components used have their respective licenses. However, to the best of our knowledge, all dependencies used have permissive licenses with no real proprietary limitations.
If you found this project relevant for your research, consider citing it by:
@software{pedersen2023fp_dsa_plugin,
author = {André Pedersen},
title = {andreped/FP-DSA-plugin: v0.0.1},
month = jul,
year = 2023,
publisher = {Zenodo},
version = {v0.0.1},
doi = {10.5281/zenodo.8124068},
url = {https://doi.org/10.5281/zenodo.8124068}
}