CDRNet: Accurate Cup-to-Disc Ratio Measurement with Tight Bounding Box Supervision in Fundus Photography

This project hosts the codes for the implementation of CDRNet: Accurate Cup-to-Disc Ratio Measurement with Tight Bounding Box Supervision in Fundus Photography Using Deep Learning [Journal] [arXiv].

Dataset

All images are located in data/glaucoma/images. Three csv files, listing images for training, validation, and testing respectively, are in data/csv folder. For a csv file named as xxx.csv, there is a json file, named as annotation_xxx.json, which includes the bounding-box annotations for the images in the csv list. The json files locate in data/csv as well.

Example of the structure of the folder for glaucoma dataset is as follows:

+ data
   + glaucoma
      + images
        - example1.png
        - example2.png
        - example3.png
        ...
      + csv
        - train.csv
        - annotation_train.json
        - validation.csv
        - annotation_validation.csv
        - testing.csv
        - annotation_testing.csv

Training

#  The experiments include RetinaNet (exp_no=0), FSIS (exp_no=1), WSIS (exp_no=2,3), and CDRNet (exp_no=4,5,6,7)

# exp_no=0,1,2,3,4,5,6,7
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 --use_env tools/train_glaucoma.py --n_exp exp_no --world-size 4

Evaluation and performance summary

# Validation and testing results for testing set and the dataset used in reader study, exp_no=0,1,2,3,4,5,6,7
CUDA_VISIBLE_DEVICES=0 python tools/eval_glaucomapy.py --n_exp exp_no

# performance summary
python tools/report_glaucoma.py

Grader study

# Additional results for grader study
python tools/eval_glaucoma_reader_study.py

# performance summary
python tools/report_glaucoma_reader_study.py

Note, this repository also includes implementation for the paper Bounding Box Tightness Prior for Weakly Supervised Image Segmentation. Please refer to this link for more details.

Citations

Please consider citing our paper in your publications if the project helps your research.

@article{wang2022cdrnet,
  title={CDRNet: accurate cup-to-disc ratio measurement with tight bounding box supervision in fundus photography using deep learning},
  author={Wang, Juan and Xia, Bin},
  journal={Multimedia Tools and Applications},
  pages={1--23},
  year={2022},
  publisher={Springer}
}

@inproceedings{wang2021bounding,
  title={Bounding box tightness prior for weakly supervised image segmentation},
  author={Wang, Juan and Xia, Bin},
  booktitle={Medical Image Computing and Computer Assisted Intervention--MICCAI 2021: 24th International Conference, Strasbourg, France, September 27--October 1, 2021, Proceedings, Part II},
  pages={526--536},
  year={2021},
  organization={Springer}
}