smallmax00/Shape_aware_Weakly-Semi_ODOC_seg

MICCAI 2022: code for 'Shape-Aware Weakly/Semi-Supervised Optic Disc and Cup Segmentation with Regional/Marginal Consistency'

Oval-Shape-Aware-Weakly-Semi-Supervised Optic-Disc-and-Cup-Segmentation

• Prepare your data.

• Download the pre-train Res2Net parameters and put it into ./lib/res_weight/

• https://drive.google.com/file/d/1hr5n4OuhAnEKXiVJy9aPbcRwMsiqASAb/view?usp=sharing

Train

• run python train.py

Test

• Download our pre-trained model's parameter then put it into your-own-path
• https://drive.google.com/file/d/1RBp5MjFnT7lIxI9Asu-a1P-yF-gMtc5p/view?usp=sharing
• run test.py

Citation

If you find our work useful or our work gives you any insights, please cite:

@InProceedings{Meng_2022_Shape,
    title     = {Shape-Aware Weakly/Semi-Supervised Optic Disc and Cup Segmentation with Regional/Marginal Consistency},
    author    = {Meng, Yanda and Chen, Xu and Zhang, Hongrun and Zhao, Yitian and Gao, Dongxu and Hamill, Barbra and Patri, Godhuli and Peto, Tunde and Madhusudhan, Savita and Zheng, Yalin},
    booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
    year      = {2022},
    organization={Springer}
}