| Citation: | LIANG L M，YU J，CHEN X，et al. Cross-level fusion gated adaptive network for retinal vessel segmentation[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（4）：1097-1109 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0410
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To address the insufficient shallow feature extraction of most existing algorithms, which results in noise, blurred vascular boundary and capillary under segmentation, a cross-level fusion gated adaptive network is proposed. Firstly, shallow feature information is fully extracted by the dense gated channel transformation module in the network with promotion of competition or cooperation of channels to avoid the loss of shallow coarse-grained feature information. Secondly, cross-dimensional interaction information of each layer is captured by cross-level fusion module to effectively aggregate multi-scale context features. Thirdly, dual adaptive feature fusion method is used to guide the feature fusion of adjacent layers effectively and suppress noise. The validation was performed on public data sets DRIVE, CHASEDB1 and STARE, and the accuracy rates were 0.9652, 0.9668 and 0.9695 respectively; the
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