VesselXnet - A lightweight and efficient encoder-decoder based model for Retinal Vessel Segmentation

Abstract

One of the contemporary issues present in medical image segmentation is the segmentation of the Retinal blood vessels. This is because many diseases can be accurately identified from the vascular structure of the retina and hence can be treated early and diagnosed thoroughly. Manual segmentation is hectic, cumbersome, time consuming and also error-prone. Hence there is a need for automatic vessel segmentation which can be a better technological advancement in the medical field. Some of the segmentation methods which were proposed previously have problems of low segmentation accuracy, incomplete segmentation and a large model size. With the progress of deep learning and convolutional neural networks several U-net based architectures were extensively used for this task which offered reliable segmentation results. In this paper, we proposed a light weight U-net based architecture which provides comparable accuracy with much less total parameters. © 2021 IEEE.