Abstract

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Title:: Bilinear non-local features combined with intermediate supervision network for retinal vessel segmentation

Author(s):: YANG Dongxu1; ZHAO Hongdong1; GENG Lixin1; YU Kuaikuai2; 1. School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China 2. Key Laboratory of Photoelectric Information Control and Safety Technology, Tianjin 300308, China

Keywords:: Keywords: diabetic retinopathy image processing retinal vessel segmentation bilinear non-local module multi-scale input intermediate supervision

Abstract:: Abstract: The accurate segmentation of retinal vessels in fundus images is of significance for the detection of various diseases and plays an important role in automated screening system for associated diseases. To address the problem that existing methods neglect to consider the complexity of the algorithm when pursuing segmentation accuracy, which leads to difficulties in deployment on resource-constrained medical devices, the number of feature channels in the convolutional layer is further reduced to lighten the segmentation network, and a bilinear non-local intermediate supervision network (BNIS-Net) is proposed. In BNIS-Net, the multi-scale images are taken as input and fused into the coding for establishing good connections between different receptive fields, and a bilinear non-local module is added to enhance the capture of relevant contextual information. During the decoding, an intermediate supervision strategy is adopted to constrain the learning of the network by providing supervision to the output of the decoding at all levels, which can effectively improve the BNIS-Net uses a parameter of 0.41 M on 3 public data sets of DRIVE, START and CHASE, and achieves DSC values of 81.02%, 81.07% and 78.15%, and AUC values of 0.983 3, 0.986 1 and 0.985 9, respectively. It was demonstrated by numerous comparative experiments and ablation studies that the method can better segment the edge details of vessels.