Resources

1. CU-Net: Towards Continuous Multi-Class Contour Detection for Retinal Layer Segmentation In Oct Images
1. ICIP poster (2022)
2. ICIP slides (2022)
3. Benchmark Dataset: S. J. Chiu, M. J. Allingham, P. S. Mettu, S. W. Cousins, J. A. Izatt, S. Farsiu, "Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema", ( BIOMEDICAL OPTICS EXPRESS), 6(4), pp. 1172-1194, April, 2015.
4. Benchmark Dataset: Benchmark Dataset: Y. He, A. Carass, S.D. Solomon, S. Saidha, P.A. Calabresi, and J.L. Prince, "Retinal layer parcellation of optical coherence tomography images: Data resource for Multiple Sclerosis and Healthy Controls", Data in Brief, 22:601-604, 2019.


2. SCR Detection
1. DEnT Slides
2. Github code (DEnT and existing methods)
3. Nemours dataset (original images and annotations)
4. Nemours dataset (Pre-processed pickles)


3. A deep learning system for sickle cell retinopathy detection using retinal OCT images from children with sickle cell disease
1. Arvo poster (2022)


4. Deep learning-based contour detection for retinal layer segmentation and sickle cell retinopathy detection in OCT images
1. Preliminary study report (2021)