MSR U-Net: An Improved U-Net Model for Retinal Blood Vessel Segmentation

Abstract

For the proper diagnosis and treatment of a variety of retinal conditions, retinal blood vessel segmentation is crucial. Delineation of vessels with varying thicknesses is critical for detecting disease symptoms. However, this task is challenging due to inadequate contextual information, complex vessel morphology, and lesion confusion. Many recent works employed several variations of CNNs with U-Net as baseline model for segmenting blood vessels from the fundus images. However, the existing methods still lack in generalizing the vessels well enough, indicating scope for improvement in this challenging problem of vessel segmentation. We introduce a novel Multi-Scale Residual (MSR) U-Net model in this study replacing convolution block and skip connections with an improved Multi-Scale Residual (MSR) convolution block and Bottleneck residual paths (B-Res paths) respectively. Specifically, STARE, DRIVE, and CHASEDB1 datasets of fundus images are used to validate the proposed segmentation method. Our experimental results consistently showcase better/comparable performances when compared with current approaches, achieving higher area under receiver operator characteristic (AUC), accuracy, and F1 score. in segmenting blood vessels of varying thicknesses, even in scenarios with diverse contextual information, the presence of coexisting lesions, and complex vessel morphologies.