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https://github.com/haranrk/digipathai
Tool to visualize gigantic pathology images and use AI to segment cancer cells and present as an overlay
https://github.com/haranrk/digipathai
cancer-research deep-learning gui medical-image-analysis openslide python segmentation wsi
Last synced: about 1 month ago
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Tool to visualize gigantic pathology images and use AI to segment cancer cells and present as an overlay
- Host: GitHub
- URL: https://github.com/haranrk/digipathai
- Owner: haranrk
- License: mit
- Created: 2019-10-04T16:12:12.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-10-08T23:17:46.000Z (over 1 year ago)
- Last Synced: 2024-12-08T05:03:07.713Z (about 2 months ago)
- Topics: cancer-research, deep-learning, gui, medical-image-analysis, openslide, python, segmentation, wsi
- Language: JavaScript
- Homepage:
- Size: 138 MB
- Stars: 66
- Watchers: 6
- Forks: 26
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
README
[](https://badge.fury.io/py/DigiPathAI)
[](https://pepy.tech/project/digipathai)
[](https://arxiv.org/abs/2001.00258)
# DigiPathAI
A software application built on top of [openslide](https://openslide.org/) for viewing [whole slide images (WSI)](https://www.ncbi.nlm.nih.gov/pubmed/30307746) and performing pathological analysis
### Citation
If you find this reference implementation useful in your research, please consider citing:
```
@article{khened2020generalized,
title={A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and Analysis},
author={Khened, Mahendra and Kori, Avinash and Rajkumar, Haran and Srinivasan, Balaji and Krishnamurthi, Ganapathy},
journal={arXiv preprint arXiv:2001.00258},
year={2020}
}
```
# Features
- Responsive WSI image viewer
- State of the art cancer AI pipeline to segment and display the cancerous tissue regions
# Application Overview
# Results
# Installation
Running of the AI pipeline requires a GPU and several deep learning modules. However, you can run just the UI as well.
## Just the UI
### Requirements
- `openslide`
- `flask`
The following command will install only the dependencies listed above.
```
pip install DigiPathAI
```
## Entire AI pipeline
### Requirements
- `pytorch`
- `torchvision`
- `opencv-python`
- `imgaug`
- `matplotlib`
- `scikit-learn`
- `scikit-image`
- `tensorflow-gpu >=1.14,<2`
- `pydensecrf`
- `pandas`
- `wget`
The following command will install the dependencies mentioned
```
pip install "DigiPathAI[gpu]"
```
Both installation methods install the same package, just different dependencies. Even if you had installed using the earlier command, you can install the rest of the dependencies manually.
# Usage
## Local server
Traverse to the directory containing the openslide images and run the following command.
```
digipathai
```
## Python API usage
The application also has an API which can be used within python to perform the segmentation.
```
from DigiPathAI.Segmentation import getSegmentation
prediction = getSegmentation(img_path,
patch_size = 256,
stride_size = 128,
batch_size = 32,
quick = True,
tta_list = None,
crf = False,
save_path = None,
status = None)
```
# Contact
- Avinash Kori ([email protected])
- Haran Rajkumar ([email protected])
[](https://ko-fi.com/M4M132RPG)