Segmentation of Retinal Images Using Improved Segmentation Network, MesU-Net

Abstract

Given the immense importance of medical image segmentation and the challenges associated with manual execution, a diverse range of automated medical image segmentation methods have been developed, primarily focusing on specific modalities of images. This paper introduces an innovative segmentation algorithm that effectively segments exudates, hemorrhages, microaneurysms, and blood vessels within retinal images using an enhanced MesNet (MesU-Net) model. By combining the MES-Net model with the U-Net model, this approach achieves accurate results in a shorter period. Consequently, it holds significant potential for clinical application in computer-aided diagnosis. The IDRID and DRIVE datasets are utilized to assess the efficacy of the proposed model for retinal segmentation. The presented method attains segmentation accuracy rates of 97.6%, 98.1%, 99.2%, and 83.7% for exudates, hemorrhages, microaneurysms, and blood vessels, respectively. This proposed model also holds promise for extension to address other medical image segmentation challenges in the future.