Deep Learning Strategies in Retinal Vessel Segmentation

Abstract

Early detection of retinal vessel abnormalities contributes in reducing the risk of early ophthalmological diseases and cardo vascular diseases. Segmentation of retinal vessel provides important information about the vascular structure of the eye which will contribute in the detection of such diseases. Current retinal vessel segmentation methods still has poor accuracy and low generalization ability, due the complexity of the vessel structure and the low contrast between the background and the vessels. A robust and generalized segmentation method remain important for improving the diagnosis of related diseases, such as vein occlusions and diabetic retinopathy. This thesis proposes to study the effect of extending a real-valued neural network based on a U-Net architecture to a hypercomplex domain, mainly quaternion-valued neural network in the task of retinal vessel segmentation. Previous work done in this area proposed different real-valued neural networks with preprocessing step to choose the most contrasting channel, mainly the green channel, and feature enhancement techniques. In this work, we will capture the interrelationship information between channels by using all the RGB channels in a quaternion domain representation. The proposed model was tested, improved and evaluated in comparison to state-of-the-art models. The results showed that the model outperformed on STARE dataset, however further improvements can be made on DRIVE and CHASE-DB1.

Date of Award	Dec 2022
Original language	American English
Supervisor	PANAGIOTIS Liatsis (Supervisor)