Artery Vein Segmentation in Handheld Fundus Camera Retinal Images and leveraging Cross Entropy for improved Semantic performance

The segmentation of retinal vessels into arteries and veins in retinal images is a crucial task for analysing the vascular changes with respect to many diseases that manifest ocular symptoms. But most existing research has concentrated on fundus images acquired using tabletop cameras and not much has been studied on images captured by handheld cameras. Such cameras are particularly useful for examining bedridden patients, especially those who may have conditions such as hypertension or diabetes that can affect the retina, since they are portable and can be easily maneuvered by healthcare providers, allowing them to perform retinal examinations conveniently at the patient’s bedside. This paper presents an approach to segment such images and assesses the impact of data augmentation on model performance. It further presents a method to compute pixel level weights during training, that allows for fine-grained adjustment of the loss function.