Please see:
- Confernce paper: MoSID: Modality-Specific Information Disentanglement from Multi-parametric MRI for Breast Tumor Segmentation (https://link.springer.com/chapter/10.1007/978-3-031-45350-2_8)
- Journal paper: Modality-Specific Information Disentanglement from Multi-parametric MRI for Breast Tumor Segmentation and Computer-aided Diagnosis (https://ieeexplore.ieee.org/document/10388458)
This project includes both train/test code for training the MoSID framwork.
- python 3.10
- pytorch 1.12.1
- numpy 1.23.3
- tensorboard 2.10.1
- simpleitk 2.1.1.1
- scipy 1.9.1
- For training the segmentation models, you need to put the data in this format:
./data
├─train.txt
├─test.txt
├─MRI1
├─ADC.nii.gz
├─T2w.nii.gz
├─P0.nii.gz
├─P2.nii.gz
└─GT.nii.gz
...
├─MRI99
└─MRI100
...
- The format of the train.txt / test.txt is as follow:
./data/train.txt
├─'MRI1'
├─'MRI2'
├─'MRI3'
...
├─'MRI100'
...
- The whole breast segmentation process can be used to remove the oversegmentation on non-breast regions.
- Partial images and whole breast annotations are available at: https://github.com/ZhangJD-ong/AI-assistant-for-breast-tumor-segmentation
If you find the code or data useful, please consider citing the following papers:
- Zhang et al., MoSID: Modality-Specific Information Disentanglement from Multi-parametric MRI for Breast Tumor Segmentation, MICCAI Workshop on Cancer Prevention through Early Detection (2023), https://doi.org/10.1007/978-3-031-45350-2_8
- Chen et al., Modality-Specific Information Disentanglement from Multi-parametric MRI for Breast Tumor Segmentation and Computer-aided Diagnosis, IEEE Transactions on Medical Imaging (2023), https://doi.org/10.1109/TMI.2024.3352648
- Zhang et al., Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches, Seminars in Cancer Biology (2023), https://doi.org/10.1016/j.semcancer.2023.09.001