https://github.com/andreped/pls-net
Reimplementation of the PLS-Net architecture used for lung lobe segmentation in CT
https://github.com/andreped/pls-net
ct deep-learning efficient lightning lobe lung lung-lobe pls-net plsnet pytorch pytorch-lightning segmentation separable-convolutions u-net
Last synced: 6 months ago
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Reimplementation of the PLS-Net architecture used for lung lobe segmentation in CT
- Host: GitHub
- URL: https://github.com/andreped/pls-net
- Owner: andreped
- License: mit
- Created: 2020-11-24T12:25:41.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2023-04-16T20:03:54.000Z (over 2 years ago)
- Last Synced: 2025-04-13T05:36:28.883Z (6 months ago)
- Topics: ct, deep-learning, efficient, lightning, lobe, lung, lung-lobe, pls-net, plsnet, pytorch, pytorch-lightning, segmentation, separable-convolutions, u-net
- Language: Python
- Homepage:
- Size: 417 KB
- Stars: 8
- Watchers: 4
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
PLS-Net (PyTorch)
Reimplementation of the PLS-Net architecture used for lung lobe segmentation in CT proposed by Lee et al. (2019).
[](https://github.com/andreped/PLS-Net/actions/workflows/test.yml)
[](https://opensource.org/licenses/MIT)
[](https://doi.org/10.1117/1.JMI.8.2.024002)
The implementation was made for this study by [Bouget et al. (2021)](https://doi.org/10.1117/1.JMI.8.2.024002). The original implementation can be found [here](https://arxiv.org/abs/1909.07474).
## [Usage](https://github.com/andreped/PLS-Net#usage)
The source code was tested in Python 3.6 with CUDA 10.0.
### [Clone repo and install requirements](https://github.com/andreped/PLS-Net#clone-repo-and-install-requirements)
```
git clone git+https://github.com/andreped/PLS-Net
cd "PLS-Net"
pip install -r requirements.txt
```
### [Define network](https://github.com/andreped/PLS-Net#define-network)
```
from PLS_pytorch import PLS
network = PLS()
```
(Alternatively) in PyTorch-Lightning:
```
from PLS_lightning import PLS
network = PLS()
```
Disclaimer: Note that the Lightning implementation contains some hardcoded setup and Dataloaders, and thus only serves as an example. However, PyTorch implementation should work out-of-the-box.
## [How to cite](https://github.com/andreped/PLS-Net#how-to-cite)
If the source code is used in any scientific publication, please, cite the following papers:
```
@article{bouget2021code,
author = {David Bouget and Andr{\'e} Pedersen and Sayied Abdol Mohieb Hosainey and Johanna Vanel and Ole Solheim and Ingerid Reinertsen},
title = {{Fast meningioma segmentation in T1-weighted magnetic resonance imaging volumes using a lightweight 3D deep learning architecture}},
volume = {8},
journal = {Journal of Medical Imaging},
number = {2},
publisher = {SPIE},
pages = {024002},
keywords = {three-dimensional segmentation, deep learning, meningioma, magnetic resonance imaging, clinical diagnosis, Magnetic resonance imaging, Image segmentation, Tumors, Brain, 3D image processing, 3D modeling, Image resolution, Data modeling, Neural networks, Surgery},
year = {2021},
doi = {10.1117/1.JMI.8.2.024002},
url = {https://doi.org/10.1117/1.JMI.8.2.024002}
}
```
```
@misc{lee2019plsnet,
author = {Lee, Hoileong and Matin, Tahreema and Gleeson, Fergus and Grau, Vicente},
title = {{Efficient 3D Fully Convolutional Networks for Pulmonary Lobe Segmentation in CT Images}},
publisher = {arXiv},
year = {2019},
copyright = {arXiv.org perpetual, non-exclusive license},
keywords = {Image and Video Processing (eess.IV), Computer Vision and Pattern Recognition (cs.CV), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Computer and information sciences, FOS: Computer and information sciences},
doi = {10.48550/ARXIV.1909.07474},
url = {https://arxiv.org/abs/1909.07474}
}
```