Retinal Blood Vessel Segmentation Method Based on Multi-scale Convolution Kernel U-Net Model

doi:10.12068/j.issn.1005-3026.2021.01.002

Abstract

Abstract: Aiming at the computer-aided diagnosis of diseased retinal vascular structure， a retinal blood vessel segmentation method of multi-scale convolution kernel U-Net model was proposed. Based on the U-Net model， a multi-scale convolutional neural network structure combining with the Inception module and the maximum index value upsampling method was designed. In the network training stage， operations such as rotation and mirroring were used to expand the data sets， and the CLAHE algorithm was used for image preprocessing. The dual-channel feature map obtained after training was normalized by Softmax. Finally， the normalized result was iteratively optimized by the improved cost loss function， then a complete retinal vessel segmentation model was obtained. Experimental results showed that the proposed method on the DRIVE data set achieved an accuracy of 0.9694， a sensitivity of 0.7762， and a specificity of 0.9835. The proposed method has better segmentation effect and generalization ability than the U-Net model， and shows its competitive results compared with other existing methods.

Key words: retinal blood vessel; multi-scale convolution kernel; U-Net model; Inception module; CLAHE algorithm

CLC Number:

TP391.41

YANG Dan， LIU Guo-ru， REN Meng-cheng， PEI Hong-yang. Retinal Blood Vessel Segmentation Method Based on Multi-scale Convolution Kernel U-Net Model[J]. Journal of Northeastern University(Natural Science), 2021, 42(1): 7-14.

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