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Retinal Vessel Segmentation via a Semantics and Multi-Scale Aggregation Network

Precise segmentation of retinal vessels is crucial for a computer-aided diagnosis system of retinal fundus images. However, this task remains challenging due to large variations in scales and poor segmentation of capillary vessels. In this paper, we propose a semantics and multi-scale aggregation network to address these difficulties. It includes semantics aggregation blocks that are designed for aggregating stronger high-level semantic information. These carefully designed blocks produce more semantic feature representation that is helpful for capillary vessel identification and vessel connection. Besides, a multi-scale aggregation block is designed by employing parallel dilated convolutional filters with different dilation rates to fully exploit the multi-scale information. We evaluate the network by using two public databases of retinal vessel segmentation and compare its performance with several leading methods published in the past several years. Extensive evaluations show that the proposed network has achieved the state-of-the-art performance on the public CHASE DB1 and HRF datasets.