| Summary: | The structural information of retinal vesselsassists in the diagnosis of ophthalmic diseases,and thus efficient and accurate segmentation of retinal vessel images has become an urgent clinical demannd.The traditional artificial segmentation methods are time-consumingand frequently affected by personal subjective factors,leading to a decline in segmentation quality.To address the problem,thispaper proposes an automatic image segmentation algorithm based on dense attention network.The algorithm combines the basic structure of the encoder-decoder fully convolutional neural network with the densely connected network to fully extract the features of each layer.Then the attention gate module on the decoder side of the network is introduced to suppress unnecessary features and thus improve the segmentation accuracy of retinal vessel segmentation.Experimental results on DRIVE and STARE fundus image datasets show that compared with other algorithms based on deep learning,the proposedalgorithm has excellent segmentation performance with the sensitivity,specificity,accuracy and AUC value all improved.
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