by Pierre Rougé, Nicolas Passat, Odyssée Merveille
Code associated with the paper Cascaded Multitask U-Net using topological loss for vessel segmentation and centerline extraction.
Install environment with environment.yml
conda env create -f environment.yml
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Clone the repository
git clone https://github.com/PierreRouge/Cascaded-U-Net-for-vessel-segmentation.git cd Cascaded-U-Net-for-vessel-segmentation
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Put the MRA Images in data/Images, the segmentations GTs in data/GT and the skeletons GTs in data/Skeletons
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Use one of the training function
- To pretrain segmentation network
cd train python train_segmentation.py
- To pretrain skeletonization network
cd train python train_skeletonization.py
- To train cascaded U-Net : put the pretrained weights in respectively pretrained_weights/segmentation and pretrained_weights/skeletonization and run :
cd train python train_cascaded_unet.py
We are currently working to implement several state-of-the-art methods for vascular segmentation !
If you use this repository please consider citing :
@article{rouge2023cascaded,
title={Cascaded multitask U-Net using topological loss for vessel segmentation and centerline extraction},
author={Roug{\'e}, Pierre and Passat, Nicolas and Merveille, Odyss{\'e}e},
journal={arXiv preprint arXiv:2307.11603},
year={2023}
}
Pierre Rougé : pierre.rouge@creatis.insa-lyon.fr