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Accurate segmentation of the blood vessels of the retina is an important stepin clinical diagnosis of ophthalmic diseases. Many deep learning frameworkshave come up for retinal blood vessels segmentation tasks. However, the complexvascular structure and uncertain pathological features make the blood vesselsegmentation still very challenging. A novel U-shaped network namedMulti-module Concatenation which is based on Atrous convolution andmulti-kernel pooling is put forward to retinal vessels segmentation in thispaper. The proposed network structure retains three layers the essentialstructure of U-Net, in which the atrous convolution combining the multi-kernelpooling blocks are designed to obtain more contextual information. The spatialattention module is concatenated with dense atrous convolution module andmulti-kernel pooling module to form a multi-module concatenation. And differentdilation rates are selected by cascading to acquire a larger receptive field inatrous convolution. Adequate comparative experiments are conducted on thesepublic retinal datasets: DRIVE, STARE and CHASE_DB1. The results show that theproposed method is effective, especially for microvessels. The code will be putout at https://github.com/Rebeccala/MC-UNet

MC-UNet Multi-module Concatenation based on U-shape Network for Retinal Blood Vessels Segmentation