Blood Vessel Segmentation Using U-Net for Glaucoma Diagnosis with Limited Data

Abstract

Glaucoma is one of the leading causes of blindness worldwide. Therefore, early detection and diagnosis are key to preserve full vision in patients. As part of the SALUS study, we create a blood vessel segmentation model based on U-Net. We trained U-Net on three different loss functions and used hyperparameter tuning to find their optimal hyperparameters for each loss function. The best models for each of the loss functions achieved an accuracy of over 93%, Dice scores around 83% and Intersection over Union scores over 70%. They each identify large blood vessels reliably and even recognize smaller blood vessels in the retinal fundus images and thus pave the way for improved glaucoma management.

Keywords: Blood vessel segmentation; Deep Learning; Glaucoma; Ophthalmology; U-Net; segmentation.