A U-shaped Retinal Vessel Segmentation Network Based on Multi-scale Global Attention
Retinal vessel segmentation plays an important role in the evaluation and diagnosis of many types of ophthalmic diseases.Due to the complex and variable topological structure of blood vessels in fundus images,existing algorithms often suffer from discontinuous vascular features in segmentation results and low accuracy in vascular edge segmentation.In response to the above issues,this paper proposes a multi-scale global attention U-shaped neural network MSGA-UNet for retinal vessel segmentation.On the one hand,this network easily obtains the global representation information of the image from the encoder through the global feature attention module,solving the problem of feature discontinuity in retinal vessel segmentation of the fundus;On the other hand,utilizing multi-scale dilated convolution modules,dilated convolutions with different dilation rates are used to expand the receptive field and obtain multi-scale local feature information of the image,thereby improving the ability to extract vascular edge information.After experiments on the DRIVE,STARE,and CHASEDB1 datasets,the average intersection to union ratios of MSGA-UNet were 74.06%,78.22%,and 79.62%,respectively;The average pixel accuracy of each category is 80.39%,84.60%,and 85.53%,respectively;The accuracy is 96.32%,96.42%,and 97.23%respectively;The comprehensive segmentation performance is superior to other models.
medical image segmentationretinal blood vesselsU-shaped networkTransformerattention
万方数据
维普
