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)
For ORIGA and SCES datasets
Unfortunately, the ORIGA and SCES datasets cannot be released due to the clinical policy.
But, here is an other glaucoma challenge, Retinal Fundus Glaucoma Challenge (REFUGE), including disc/cup segmentation, glaucoma screening, and localization of Fovea. If you are interested, you can register it from: [HERE]
We also provide the results of our MNet, the details could be found from: [HERE]
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.