A full scale dense convolutional U-shaped network based retinal blood vessel segmentation method was proposed to address the issues of large blood vessel scale span and similarity between small blood vessels and background height in retinal images,resulting in mis-segmentation and non-segmentation.To extract more complex feature information,cascade convolutional fusion dense blocks(CCF-DB)were constructed as the codec of the U-shaped network to extract the feature information of reti-nal blood vessels.The mixed attention cascaded convolutional density block(MACC-DB)was embedded at the bottom of the net-work to further enhance the receptive field and obtain higher dimensional semantic feature information.In the decoding part of the model,a full scale skip connection was used to capture vascular feature information at different scales,improving the seg-mentation accuracy of the model.Experimental results show that on the DRIVE dataset,compared to algorithms such as U-Net,U-Net3+,SA-Unet,FR-Unet,etc.,the AUC value of this algorithm reaches 98.26％,with an accuracy of 95.82％.On the CHASE-DB1 dataset,the AUC value of this algorithm reaches 98.84％,with an accuracy rate of 96.66％.Therefore,using this algorithm for retinal vessel segmentation improves the accuracy and robustness to varying degrees,achieving excellent results for small vessel segmentation.
关键词
医学图像分割/深度学习/视网膜血管分割/全尺度密集卷积/编解码结构/混合注意力/级联卷积
Key words
medical image segmentation/deep learning/retinal vascular segmentation/full scale dense convolution/encoding and decoding structure/mixed attention/cascade convolution
维普
万方数据