[1]王志鲁,池越,周亚同,等.融合密集空洞注意力金字塔和多尺度的视网膜病变分割[J].中国医学物理学杂志,2024,41(8):1000-1009.[doi:DOI:10.3969/j.issn.1005-202X.2024.08.013]
　WANG Zhilu,CHI Yue,ZHOU Yatong,et al.Diabetic retinopathy segmentation using dense dilated attention pyramid and multi-scale features[J].Chinese Journal of Medical Physics,2024,41(8):1000-1009.[doi:DOI:10.3969/j.issn.1005-202X.2024.08.013]
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融合密集空洞注意力金字塔和多尺度的视网膜病变分割()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
41卷
期数:
2024年第8期
页码:
1000-1009
栏目:
医学影像物理
出版日期:
2024-08-31

文章信息/Info

Title:
Diabetic retinopathy segmentation using dense dilated attention pyramid and multi-scale features
文章编号:
1005-202X（2024）08-1000-10
作者:
王志鲁1; 池越1; 周亚同1; 单春艳2; 肖志涛3; 王劭奇1
1.河北工业大学电子信息工程学院， 天津 300401； 2.天津医科大学朱宪彝纪念医院/天津市内分泌研究所/国家卫健委激素与发育重点实验室/天津市代谢性疾病重点实验室， 天津 300134； 3.天津工业大学生命科学学院， 天津 300387
Author(s):
WANG Zhilu1;  CHI Yue1;  ZHOU Yatong1;  SHAN Chunyan2;  XIAO Zhitao3;  WANG Shaoqi1
1. School of Electronic and Information Engineering, Hebei University of Technology, Tianjin 300401, China 2. Tianjin Medical University Chu Hsien-I Memorial Hospital/Tianjin Institute of Endocrinology/Key Laboratory of Hormones and Development of the National Health Commission/Tianjin Key Laboratory of Metabolic Diseases, Tianjin 300134, China 3. School of Life Sciences, Tiangong University, Tianjin 300387, China
关键词:
糖尿病视网膜病变; 密集空洞注意力金字塔; 多尺度特征; 残差模块
Keywords:
Keywords: diabetic retinopathy dense dilated attention pyramid multi-scale feature residual module
分类号:
R318；TP391.41
DOI:
DOI:10.3969/j.issn.1005-202X.2024.08.013
文献标志码:
A
摘要:
针对糖尿病视网膜病变（DR）分割任务中病变区域多尺度特征难以学习、边界模糊等问题，提出一种改进的U型多病变分割模型DDAPNet。首先，对DR图像进行Patch处理，使模型更好地捕捉病变的局部特征；其次在主干特征提取后引入重新设计的密集空洞注意力金字塔（DDAP）模块，扩大感受野，解决病变边界模糊问题；同时采用金字塔切分注意力进行特征增强，然后将二者进行特征融合；最后在跳跃连接中嵌入改进的残差注意力模块，降低浅层冗余信息的干扰。在数据集和医院真实数据集上进行联合验证，实验结果表明，相较于基础模型，DDAPNet模型对微动脉瘤、出血点、软渗出DDR物和硬渗出物的分割在Dice系数上分别提高了4.31%、2.52%、3.39%、4.29%，在mIoU上分别提高了1.80%、2.24%、4.28%、1.98%。该模型对病灶边缘的分割更为连续和平滑，有效提升了软渗出物等视网膜病变的分割性能。
Abstract:
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 models 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.

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备注/Memo

备注/Memo:
【收稿日期】2024-01-10 【基金项目】京津冀基础研究合作专项（J210008, 21JCZXJC00170, H2021202008）；天津市医学重点学科（专科）建设项目（TJYXZDXK-032A） 【作者简介】王志鲁，硕士研究生，研究方向：医学图像处理、机器学习，E-mail: 2516153604@qq.com 【通信作者】池越，副教授，研究方向：信息感知与机器学习，E-mail: chiyueliuxin@126.com
更新日期/Last Update: 2024-08-31