| # CoactSeg: Learning from Heterogeneous Data for New Multiple Sclerosis Lesion Segmentation |
| by Yicheng Wu*, Zhonghua Wu, Hengcan Shi, Bjoern Picker, Winston Chong, and Jianfei Cai. |
| |
| ### News |
| ``` |
| <26.07.2023> Due to IP restrictions, the data sharing is suspended now. |
| <11.07.2023> We release the codes. |
| ``` |
| |
| ### Introduction |
| This repository is for our MICCAI 2023 paper: '[CoactSeg: Learning from Heterogeneous Data for New Multiple Sclerosis Lesion Segmentation](https://doi.org/10.1007/978-3-031-43993-3_1)' (Early Acceptance, top 14%). |
| |
| ### Environment |
| This repository is based on PyTorch 1.8.0, CUDA 11.1, and Python 3.8.10. All experiments in our paper were conducted on a single NVIDIA Tesla V100 GPU with an identical experimental setting. |
| |
| ### Data Preparation |
| Please obtain the original public [MSSEG-2](https://portal.fli-iam.irisa.fr/msseg-2/data/) Dataset. Then, the [HD-BET](https://github.com/MIC-DKFZ/HD-BET) tool is used to extract the brain regions. We further apply the re-sampling and z-score normalization operations [here](https://github.com/ycwu1997/CoactSeg/blob/Main/data/MSSEG2/h5/pre_processing.py). The data split is fixed and given in 'CoactSeg/data'. |
| |
| ### Usage |
| 1. Clone the repository; |
| ``` |
| git clone https://github.com/ycwu1997/CoactSeg.git |
| ``` |
| 2. Train the model; |
| ``` |
| sh train_mixed.sh |
| ``` |
| 3. Test the model; |
| ``` |
| sh test_mixed.sh |
| ``` |
| |
| ### Citation |
| If our model is useful for your research, please consider citing: |
| ``` |
| @inproceedings{wu2023coact, |
| title={CoactSeg: Learning from Heterogeneous Data for New Multiple Sclerosis Lesion Segmentation}, |
| author={Wu, Yicheng and Wu, Zhonghua and Shi, Hengcan and Picker, Bjoern and Chong, Winston and Cai, Jianfei}, |
| booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention}, |
| pages={3--13}, |
| volume={14227}, |
| year={2023}, |
| doi={https://doi.org/10.1007/978-3-031-43993-3\_1}, |
| organization={Springer, Cham} |
| } |
| ``` |
| |
| ### Issues |
| The current training stage is slow and there is a trick when generating the second-time-point all-lesion result on the MSSEG-2 dataset (see [lines](https://github.com/ycwu1997/CoactSeg/blob/Main/code/utils/test_patch.py) 65-66). That's because two-time-point all-lesion labels are not available for the model training and the model cannot identify such slight all-lesion differences at different time points. |
| |
| We are addressing the training efficiency and the input disentanglement problems. The improved CoactSeg model will be released soon. |
| |
| If any other questions, feel free to contact me at 'ycwueli@gmail.com' |
| |
| ### Acknowledgement |
| This repository is based on our previous [MC-Net](https://github.com/ycwu1997/MC-Net). We here also appreciate the public repositories of [SNAC](https://github.com/marianocabezas/msseg2) and [Neuropoly](https://github.com/ivadomed/ms-challenge-2021), and also thanks for the efforts to collect and share the [MSSEG-2](https://portal.fli-iam.irisa.fr/msseg-2/) dataset and our MS-23v1 dataset from Alfred Health, Australia. |
| |
