An Integrated Fundus Image Segmentation Algorithm for Multiple Eye Ailments

Abstract

The detection of eye illnesses requires a thorough inspection of all of the eye's structures. Most significantly, the presence of blood vessels, an optical disc, and any other unwelcome objects, if any are discovered, is critical in determining the type of eye disease present. Specifically, the goal of this research is to establish a thorough segmentation framework that will aid in the detection of anatomical anomalies in the eye. A novel segmentation technique for analyzing blood vessels, optical disc health, and the presence of exudates has been added into the software. - It was decided to add the detection of aberrant objects such as exudates into the algorithm in order to develop a generic segmentation method. In order to verify the accuracy of the segmentation at each stage, random sampling is employed at each stage. The segmentation is then validated using the intersection over union metric. The accuracy of the integrated segmentation method as a whole is 91.66 percent.

A Novel Medical Image Segmentation Model with Domain Generalization Approach

“…Domain adaption (DA) and domain generalisation (DG) strategies have been developed to reduce the distribution incompatibility across domains to relieve the problem of domain shift. With enough labelled data from a relevant but unrelated source domain, DA seeks to enhance its performance in its target domain [2]. Distribution alignment and style transfer are common techniques used by DA systems to learn domain-invariant representations.…”