Code for TMI 2018 "Disc-aware Ensemble Network for Glaucoma Screening from Fundus Image"

Project homepage：http://hzfu.github.io/proj_glaucoma_fundus.html

1. The code is based on: Keras 2.0 + Tensorflow 1.0
2. The deep output is raw segmentation result without ellipse fitting.
3. The pre-train models are trained on ORIGA full dataset.
4. Download the trained models for DENet to 'pre_model' folder: [OneDrive] [BaiduPan]:
   1. Disc detection model: 'pre_model_DiscSeg.h5'
   2. Global image Screening model: 'pre_model_img.h5'
   3. Segmentation-guided Screening model: 'pre_model_disc.h5'
   4. Local disc Screening model: 'pre_model_ROI.h5'
   5. Polar disc Screening model: 'pre_model_flat.h5'

If you use this code, please cite the following papers:

[1] 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)

[2] 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)

For ORIGA and SCES datasets

Unfortunately, the ORIGA, SCES, and SINDI 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 DENet, the details could be found from: [HERE]

Update log:

• 18.07.06: Released the code.