https://github.com/fk128/sarcopenia-ai
Code for paper Kanavati, F. et al. (2020). Fully-automated deep learning slice-based muscle estimation from CT images for sarcopenia assessment. arXiv preprint arXiv:2006.06432.
https://github.com/fk128/sarcopenia-ai
ct-slice-detection deep-learning medical-image-analysis muscle-estimation sarcopenia-assessment
Last synced: about 2 months ago
JSON representation
Code for paper Kanavati, F. et al. (2020). Fully-automated deep learning slice-based muscle estimation from CT images for sarcopenia assessment. arXiv preprint arXiv:2006.06432.
- Host: GitHub
- URL: https://github.com/fk128/sarcopenia-ai
- Owner: fk128
- License: other
- Created: 2020-08-28T17:23:30.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2023-05-26T20:18:46.000Z (over 2 years ago)
- Last Synced: 2024-12-06T21:19:11.907Z (10 months ago)
- Topics: ct-slice-detection, deep-learning, medical-image-analysis, muscle-estimation, sarcopenia-assessment
- Language: Python
- Homepage: https://arxiv.org/abs/2006.06432
- Size: 69.9 MB
- Stars: 29
- Watchers: 4
- Forks: 14
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Sarcopenia AI
Code for papers:
Kanavati, F., Islam, S., Aboagye, E. O., & Rockall, A. (2018). Automatic L3 slice detection in 3D CT images using fully-convolutional networks. arXiv preprint arXiv:1811.09244.
Kanavati, F., Islam, S., Arain, Z., Aboagye, E. O., & Rockall, A. (2020).
Fully-automated deep learning slice-based muscle estimation from CT images for sarcopenia assessment. arXiv preprint arXiv:2006.06432.
### Models
Trained models for slice detection and slice segmentation are provided in `models/`
### Dev
```
conda create -y --name sarcopenia-ai python=3.6.2
```
Install
```bash
pip install -e .
```
### Docker
#### Build docker image
```bash
docker build -t sarcopeniaai -f ./Dockerfile .
```
or `make build`
#### Slice detection trainer
Download the training data from [here](https://imperialcollegelondon.box.com/s/0vt07mxy0re4zwao0sk76ywdt2s1pclm)
to your data folder.
```
docker run --rm -it -v :/data -v $(pwd)/configs:/configs sarcopeniaai python -m sarcopenia_ai.apps.slice_detection.trainer --config /configs/slice_detection.cfg
```
Training output preview on validation images
 
#### Segmentation trainer
Labelled segmentation data is not provided. Once you get your own data, you can train a segmentation model with
```bash
docker run --rm -it -v :/data -v $(pwd)/configs:/configs sarcopeniaai python -m sarcopenia_ai.apps.segmentation.trainer --config /configs/segmentation.cfg
```
#### Run as API server
`docker run --rm -it -p 5000:5000 sarcopeniaai python -m sarcopenia_ai.apps.server.run_local_server`
or `make server`
Then head to http://localhost:5000 for web UI
You can also get results from command line. Example:
`curl -X POST -F image=@data/volume.nii.gz http://localhost:5000/predict`
Expected result
```json
{
"prediction":{
"id":"64667bf3482d4ee5a0e8af6c67b2fa0d",
"muscle_area":"520.15",
"muscle_attenuation":"56.00 HU",
"slice_prob":"69.74%",
"slice_z":90,
"str":"Slice detected at position 90 of 198 with 69.74% confidence "
},
"success":true
}
```
### L3 annotated dataset
The dataset was collected from multiple sources:
1. 3 sets were obtained from [the Cancer Imaging Archive (TCIA)](http://www.cancerimagingarchive.net/):
- [head and neck](http://doi.org/10.7937/K9/TCIA.2017.umz8dv6s)
- [ovarian](http://dx.doi.org/10.7937/K9/TCIA.2016.NDO1MDFQ)
- [colon](http://doi.org/10.7937/K9/TCIA.2015.NWTESAY1)
2. a liver tumour dataset was obtained from the
[LiTS segmentation challenge](https://competitions.codalab.org/competitions/17094).
The dataset is available for download in MIPs format from
[here](https://imperialcollegelondon.box.com/s/0vt07mxy0re4zwao0sk76ywdt2s1pclm).
The subset of transitional vertabrae cases can be downloaded from
[here](https://imperialcollegelondon.box.com/s/mw7ysamajjcp1ot0721e6nl36xku0acv).
```
@article{kanavati2018automatic,
title={Automatic L3 slice detection in 3D CT images using fully-convolutional networks},
author={Kanavati, Fahdi and Islam, Shah and Aboagye, Eric O and Rockall, Andrea},
journal={arXiv preprint arXiv:1811.09244},
year={2018}
}
@article{kanavati2020fullyautomated,
title={Fully-automated deep learning slice-based muscle estimation from CT images for sarcopenia assessment},
author={Fahdi Kanavati and Shah Islam and Zohaib Arain and Eric O. Aboagye and Andrea Rockall},
year={2020},
eprint={2006.06432},
archivePrefix={arXiv},
primaryClass={eess.IV}
}
```