Diabetic retinopathy segmentation using dense dilated attention pyramid and multi-scale features
10.3969/j.issn.1005-202X.2024.08.013
- VernacularTitle:融合密集空洞注意力金字塔和多尺度的视网膜病变分割
- Author:
Zhilu WANG
1
;
Yue CHI
;
Yatong ZHOU
;
Chunyan SHAN
;
Zhitao XIAO
;
Shaoqi WANG
Author Information
1. 河北工业大学电子信息工程学院,天津 300401
- Keywords:
diabetic retinopathy;
dense dilated attention pyramid;
multi-scale feature;
residual module
- From:
Chinese Journal of Medical Physics
2024;41(8):1000-1009
- CountryChina
- Language:Chinese
-
Abstract:
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.
