Diabetic retinopathy segmentation using dense dilated attention pyramid and multi-scale features
An improved U-shaped multi-lesion segmentation model,namely dense dilated attention pyramid UNet(DDAPNet),is proposed to overcome the difficulty in learning multi-scale features and address the issue of blurry boundaries in diabetic retinopathy(DR)segmentation task.DR images are treated with Patch processing to enhance the model's ability to capture local lesion features.After backbone feature extraction,a redesigned dense dilated attention pyramid module is introduced to expand the receptive field and address the issue of blurry lesion boundaries;and simultaneously,pyramid split attention module is used for feature enhancement;and then,the features output by the two modules are fused.Additionally,an improved residual attention module is embedded within skip connections to reduce interference from shallow redundant information.The joint validation on DDR dataset and real dataset from a specific hospital shows that compared with the original model,DDAPNet model improves the Dice similarity coefficient for segmentations of microaneurysms,hemorrhages,soft exudates and hard exudates by 4.31%,2.52%,3.39%and 4.29%,respectively,and increases mean intersection over union by 1.80%,2.24%,4.28%and 1.98%,respectively.The proposed model makes the segmentation of lesion edges smoother and more continuous,notably enhancing the segmentation performance for conditions like soft exudates in retinal lesions.
NETL
NSTL
万方数据
