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https://github.com/andreped/fp-dsa-plugin
Digital Slide Archive plugin to enable FAST deployment of pretrained CNNs for digital pathology
https://github.com/andreped/fp-dsa-plugin
cpp deep-learning digital-pathology digital-slide-archive fast fastpathology gpu histopathology large-image plugin python real-time tensorrt
Last synced: about 2 months ago
JSON representation
Digital Slide Archive plugin to enable FAST deployment of pretrained CNNs for digital pathology
- Host: GitHub
- URL: https://github.com/andreped/fp-dsa-plugin
- Owner: andreped
- License: mit
- Created: 2023-02-20T21:17:42.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-11-16T14:58:08.000Z (about 1 year ago)
- Last Synced: 2024-05-21T06:14:18.919Z (8 months ago)
- Topics: cpp, deep-learning, digital-pathology, digital-slide-archive, fast, fastpathology, gpu, histopathology, large-image, plugin, python, real-time, tensorrt
- Language: Python
- Homepage:
- Size: 6.77 MB
- Stars: 5
- Watchers: 6
- Forks: 1
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# FastPathology Digital Slide Archive (FP-DSA) extension
[](https://opensource.org/licenses/MIT)
[](https://doi.org/10.5281/zenodo.8124068)
**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.
## 🎊 Features
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
## 🐳 Requirements
DSA needs to be installed. Follow the instructions [here](https://github.com/DigitalSlideArchive/digital_slide_archive/tree/master/devops/dsa) 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
```
## 💻 Installation
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.
## 👏 Acknowledgements
The core was built based on [pyFAST](https://github.com/smistad/FAST), and the plugin was inspired by the plugins made for [MONAILabel](https://github.com/Project-MONAI/MONAILabel/tree/main/plugins/dsa) and [HistomicsTK](https://github.com/DigitalSlideArchive/HistomicsTK/tree/master/histomicstk/cli). Conversion of pyFAST's pyramidal TIFF annotations to HistomicsTK's JSON annotations was enabled using [OpenCV](https://github.com/opencv/opencv).
The plugin was made for the [Digital Slide Archive](https://github.com/DigitalSlideArchive/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](https://github.com/manthey) and [dgutman](https://github.com/dgutman) for addressing any issue and concerns we had at impressive speed!
## ✨ License
The plugin has [MIT-License](https://github.com/andreped/FP-dsa-plugin/blob/main/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.
## 🔬 Citation
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}
}
```