Code for TMI 2018 "Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation"
Project homepage：http://hzfu.github.io/proj_glaucoma_fundus.html
- The code is based on: Keras 2.0 + Tensorflow 1.0
- The deep output is raw segmentation result without ellipse fitting.
- The ellipse fitting is included in matlab code (by using PDollar toolbox: https://pdollar.github.io/toolbox/).
- The code includes (A) Disc detection from whole image and (B) Disc/Cup segmentation from ROI region (size: 800x800).
- The pre-train models 'Model_DiscSeg_ORIGA_pretrain.h5' and 'Model_MNet_ORIGA_pretrain.h5' are trained on ORIGA full dataset.
- The cup-to-disc ratio (CDR) can be calculated by segmentation result (based on Matlab).
If you use this code, please cite the following papers:
[1] Huazhu Fu, Jun Cheng, Yanwu Xu, Damon Wing Kee Wong, Jiang Liu, and Xiaochun Cao, "Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation", IEEE Transactions on Medical Imaging (TMI), vol. 37, no. 7, pp. 1597–1605, 2018. (ArXiv version)
[2] Huazhu Fu, Jun Cheng, Yanwu Xu, Changqing Zhang, Damon Wing Kee Wong, Jiang Liu, and Xiaochun Cao, "Disc-aware Ensemble Network for Glaucoma Screening from Fundus Image", IEEE Transactions on Medical Imaging (TMI), 2018. DOI: 10.1109/TMI.2018.2837012 (ArXiv version)
Update log:
- 18.06.30: Added ellipse fitting code (based on Matlab), and Fixed the bug for macular center fundus.
- 18.06.29: Added disc detection code (based on U-Net).
- 18.02.26: Added CDR calculation code (based on Matlab).
- 18.02.24: Released the code.