Tools for extracting the raw optical coherence tomography (OCT) and fundus data from proprietary file formats.

Project for segmentation of blood vessels, microaneurysm and hardexudates in fundus images.

[MICCAI'21] [Tensorflow] Retinal Vessel Segmentation using a Novel Multi-scale Generative Adversarial Network

Patho-GAN: interpretation + medical data augmentation. Code for paper work "Explainable Diabetic Retinopathy Detection and Retinal Image Generation"

exudates detection using hybrid approach (Image Morphology & Machine Learning)

PyTorch implementation for our paper on TMI2022: Retinal Vessel Segmentation with Skeletal Prior and Contrastive Loss

Actively maintained and comprehensive public glaucoma dataset catalog

A python package to read, analyse and visualize OCT and fundus data from various sources.

The Hamilton Eye Institute Macular Edema Dataset (HEI-MED) (formerly DMED) is a collection of 169 fundus images to train and test image processing algorithms for the detection of exudates and diabetic macular edema. The images have been collected as part of a telemedicine network for the diagnosis of diabetic retinopathy

Standardized Multi-Channel Dataset for Glaucoma (SMDG-19) is a collection and standardization of 19 public full-fundus glaucoma images and associated metadata.

Optic disk is detected using image processing

Official repository of the paper "RRWNet: Recursive Refinement Network for Effective Retinal Artery/Vein Segmentation and Classification".

Helper framework for Medical Image Analysis

An implementation of "Fundus Image Transformation Revisited: Towards Determining More Accurate Registrations" in MATLAB.