Retinal Vessel Segmentation with Multi-layer Feature Fusion
To accurately segment retinal vessels from retinal images and achieve performance improvement in image segmentation tasks,a nested U-shaped network(NestedNet)was proposed:the expression and feature fusion ability of the network was enhanced by capturing high-level features at multiple levels;based on the encoder-decoder structure of the U-shaped network,the Nested-Net adopts a three-layer nesting to form an inverted pyramid structure,and the encoder output of the two outermost U-shaped struc-ture was passed to the encoder of the next layer;the Addition operation of the decoder and the next encoder constitutes multiple paths from the input to the output in order to enrich the features,promoted the feature transfer and fusion,and enhanced the image expression capability;the Parallel Residual Attention Mechanism(PRAM)enhanced the network's understanding of local and global structures to generate more accurate predictions.Experimental results on the DRIVE and CHASE_DB1 datasets show that the average accuracy reaches 0.9576 and 0.9691,respectively,the area under the curve(AUC)of the subjects'work characteristics is 0.9819 and 0.9901,and the area under the curve(P-R AUC)of the precision-recall ratio is 0.9182 and 0.9411,which performs well on multiple test metrics with better performance.
万方数据
维普
