https://github.com/fepegar/resseg-ijcars
Code, data and model for Pérez-García et al. 2021, "A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections"
https://github.com/fepegar/resseg-ijcars
computer-vision convolutional-neural-networks epilepsy medical-image-computing mri segmentation tumor
Last synced: 8 months ago
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Code, data and model for Pérez-García et al. 2021, "A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections"
- Host: GitHub
- URL: https://github.com/fepegar/resseg-ijcars
- Owner: fepegar
- License: mit
- Created: 2021-01-04T18:27:46.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2024-02-06T23:32:37.000Z (over 1 year ago)
- Last Synced: 2024-02-07T00:32:00.644Z (over 1 year ago)
- Topics: computer-vision, convolutional-neural-networks, epilepsy, medical-image-computing, mri, segmentation, tumor
- Language: Python
- Homepage: https://link.springer.com/article/10.1007/s11548-021-02420-2
- Size: 47.9 KB
- Stars: 12
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## RESSEG: segmentation of postoperative brain cavities on 3D MRI using deep learning
This is the code for [Pérez-García et al., 2021, *A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections* - International Journal of Computer Assisted Radiology and
Surgery (IJCARS)](https://doi.org/10.1007/s11548-021-02420-2).
If you use this code or the [EPISURG](https://github.com/fepegar/SlicerEPISURG)
dataset for your research, please cite this publication as:
> Pérez-García, F., Dorent, R., Rizzi, M. et al. A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections. Int J CARS (2021). https://doi.org/10.1007/s11548-021-02420-2
BibTeX:
```bibtex
@article{perez-garcia_self-supervised_2021,
title = {A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections},
issn = {1861-6429},
url = {https://doi.org/10.1007/s11548-021-02420-2},
doi = {10.1007/s11548-021-02420-2},
language = {en},
urldate = {2021-06-14},
journal = {International Journal of Computer Assisted Radiology and Surgery},
author = {P{\'e}rez-Garc{\'i}a, Fernando and Dorent, Reuben and Rizzi, Michele and Cardinale, Francesco and Frazzini, Valerio and Navarro, Vincent and Essert, Caroline and Ollivier, Ir{\`e}ne and Vercauteren, Tom and Sparks, Rachel and Duncan, John S. and Ourselin, S{\'e}bastien},
month = jun,
year = {2021},
}
```
## Installation
```shell
$ conda create -n ijcars python=3.7 ipython -y && conda activate ijcars
$ conda install pytorch=1.7.1 torchvision=0.8.2 cudatoolkit=11.0 -c pytorch -y
$ pip install -r requirements.txt
```
## Related projects
### `resseg`
The trained models can be used to segment easily using [`resseg`](https://github.com/fepegar/resseg).
### `resector`
Resections can be simulated on 3D MRI using [`resector`](https://github.com/fepegar/resector).
### EPISURG dataset
See the [EPISURG extension for 3D Slicer](https://github.com/fepegar/SlicerEPISURG).
## Commands used for training
### Using simulated resections only
#### Augmentation
```
python main.py with config/simulated/config_simulated_no_augment.yml
```
#### Shape
##### Cuboids
```
python main.py with config/simulated/shape/config_simulated_shape_cuboid.yml
```
##### Ellipsoids
```
python main.py with config/simulated/shape/config_simulated_shape_ellipsoid.yml
```
##### Noisy ellipsoids (baseline)
```
python main.py with config/simulated/config_simulated_baseline.yml
```
#### Texture
##### Percentile 1
```
python main.py with config/simulated/texture/config_simulated_texture_dark.yml
```
##### Percentile 1, 99
```
python main.py with config/simulated/texture/config_simulated_texture_random.yml
```
##### CSF (baseline)
```
python main.py with config/simulated/config_simulated_baseline.yml
```
##### CSF + WM
```
python main.py with config/simulated/texture/config_simulated_texture_wm.yml
```
##### CSF + BC
```
python main.py with config/simulated/texture/config_simulated_texture_clot.yml
```
##### CSF + WM + BC
```
python main.py with config/simulated/texture/config_simulated_texture_clot_wm.yml
```
### Using clinical data from hospitals in UK, Italy and France
#### Train
#### Load and tune
```
python main.py with config/real/config_load_train.yml with dataset_name $DATASET
```