[1]杭益柳,张琼,邱建林,等.三空间注意力的残差U-Net在视网膜血管分割应用[J].中国医学物理学杂志,2024,41(6):724-733.[doi:DOI:10.3969/j.issn.1005-202X.2024.06.010]
 HANG Yiliu,ZHANG Qiong,QIU Jianlin,et al.Application of residual U-Net combined with three-space attention in retinal vessel segmentation[J].Chinese Journal of Medical Physics,2024,41(6):724-733.[doi:DOI:10.3969/j.issn.1005-202X.2024.06.010]
点击复制

三空间注意力的残差U-Net在视网膜血管分割应用()
分享到:

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
41卷
期数:
2024年第6期
页码:
724-733
栏目:
医学影像物理
出版日期:
2024-06-25

文章信息/Info

Title:
Application of residual U-Net combined with three-space attention in retinal vessel segmentation
文章编号:
1005-202X(2024)06-0724-10
作者:
杭益柳1张琼1邱建林12杨雨薇1
1.南通理工学院计算机与信息工程学院, 江苏 南通 226000; 2.南通大学信息科学技术学院, 江苏 南通 226000
Author(s):
HANG Yiliu1 ZHANG Qiong1 QIU Jianlin1 2 YANG Yuwei1
1. School of Computer and Information Engineering, Nantong Institute of Technology, Nantong 226000, China 2. School of Information Science and Technology, Nantong University, Nantong 226000, China
关键词:
视网膜血管深度学习多层次残差三空间注意力U-Net
Keywords:
Keywords: retinal vessel deep learning multi-level residual three-space attention U-Net
分类号:
R318;TP391.41
DOI:
DOI:10.3969/j.issn.1005-202X.2024.06.010
文献标志码:
A
摘要:
针对视网膜图像末端微小血管对比度低、分割不精确问题,提出一种融合多层次残差与三空间注意力机制的U型网络用于视网膜眼底血管分割。该网络在编码部分为了减少图像特征的丢失,引入原始图像后添加多层次残差模块。此外,为防止网络深层产生梯度弥散与特征数据冗余问题,在残差模块中进一步加入批量归一化与Dropout功能。在解码部分,采用三空间注意力机制来赋予类原始图像、下采样图像和上采样图像特征不同的权重,以增强特征纹理和位置信息,并实现微小血管的精确分割。实验结果显示,在公开的彩色眼底图像数据集上,与现有算法相比,本文算法的准确率、特异性、灵敏度和AUC分别为0.985、0.991、0.829和0.985,与金标准分割结果进行对比得到的血管图具有重要的临床参考价值。
Abstract:
To addresses the issues of low contrast and inaccurate segmentation of tiny vessels in retinal images, a U-shaped network incorporating multi-level residuals and three-space attention mechanism is proposed. In encoding stage, a multi-level residual module is added after inputting original images for preserving image features, and additionally, batch normalization and Dropout are integrated into the residual module to prevent vanishing gradient and feature data redundancy within the deep network. In decoding stage, a three-space attention mechanism is adopted to assign different weights to the features from the original images, down-sampled images, and up-sampled images, thus enhancing feature texture and position information, and achieving precise segmentation of tiny blood vessels. Experimental results on a public color fundus image dataset demonstrate that the proposed algorithm achieves higher accuracy (0.985), specificity (0.991), sensitivity (0.829), and AUC (0.985) than the existing algorithms. Moreover, the vessel maps obtained by the comparison with the gold standard are of significant reference value in clinic.

相似文献/References:

[1]陶源,王佳飞,杜俊龙,等.基于卷积神经网络的细胞识别[J].中国医学物理学杂志,2017,34(1):53.[doi:10.3969/j.issn.1005-202X.2017.01.011]
 [J].Chinese Journal of Medical Physics,2017,34(6):53.[doi:10.3969/j.issn.1005-202X.2017.01.011]
[2]门阔,戴建荣. 利用深度反卷积神经网络自动勾画放疗危及器官[J].中国医学物理学杂志,2018,35(3):256.[doi:DOI:10.3969/j.issn.1005-202X.2018.03.002]
 MEN Kuo,DAI Jianrong. Automatic segmentation of organs at risk in radiotherapy using deep deconvolutional neural network[J].Chinese Journal of Medical Physics,2018,35(6):256.[doi:DOI:10.3969/j.issn.1005-202X.2018.03.002]
[3]邓金城,彭应林,刘常春,等. 深度卷积神经网络在放射治疗计划图像分割中的应用[J].中国医学物理学杂志,2018,35(6):621.[doi:DOI:10.3969/j.issn.1005-202X.2018.06.001]
 DENG Jincheng,PENG Yinglin,LIU Changchun,et al. Application of deep convolution neural network in radiotherapy planning image segmentation[J].Chinese Journal of Medical Physics,2018,35(6):621.[doi:DOI:10.3969/j.issn.1005-202X.2018.06.001]
[4]查雪帆,杨丰,吴俣南,等. 结合迁移学习与深度卷积网络的心电分类研究[J].中国医学物理学杂志,2018,35(11):1307.[doi:DOI:10.3969/j.issn.1005-202X.2018.11.013]
 ZHA Xuefan,YANG Feng,WU Yunan,et al. ECG classification based on transfer learning and deep convolution neural network[J].Chinese Journal of Medical Physics,2018,35(6):1307.[doi:DOI:10.3969/j.issn.1005-202X.2018.11.013]
[5]刘海坤,王健,杨嵩,等. 基于深度学习和二维高斯拟合的视网膜血管管径测量方法[J].中国医学物理学杂志,2019,36(2):171.[doi:DOI:10.3969/j.issn.1005-202X.2019.02.010]
 LIU Haikun,WANG Jian,YANG Song,et al. Retinal vessel diameter measurement based on depth learning and two-dimensional Gaussian fitting[J].Chinese Journal of Medical Physics,2019,36(6):171.[doi:DOI:10.3969/j.issn.1005-202X.2019.02.010]
[6]宫进昌,赵尚义,王远军. 基于深度学习的医学图像分割研究进展[J].中国医学物理学杂志,2019,36(4):420.[doi:DOI:10.3969/j.issn.1005-202X.2019.04.010]
 GONG Jinchang,ZHAO Shangyi,WANG Yuanjun.Research progress on deep learning-based medical image segmentation[J].Chinese Journal of Medical Physics,2019,36(6):420.[doi:DOI:10.3969/j.issn.1005-202X.2019.04.010]
[7]安莹,黄能军,杨荣,等. 基于深度学习的心血管疾病风险预测模型[J].中国医学物理学杂志,2019,36(9):1103.[doi:DOI:10.3969/j.issn.1005-202X.2019.09.021]
 AN Ying,HUANG Nengjun,YANG Rong,et al. Deep learning-based model for risk prediction of cardiovascular diseases[J].Chinese Journal of Medical Physics,2019,36(6):1103.[doi:DOI:10.3969/j.issn.1005-202X.2019.09.021]
[8]徐航,随力,张靖雯,等.卷积神经网络在医学图像分割中的研究进展[J].中国医学物理学杂志,2019,36(11):1302.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.011]
 XU Hang,SUI Li,ZHANG Jingwen,et al.Progress on convolutional neural network in medical image segmentation[J].Chinese Journal of Medical Physics,2019,36(6):1302.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.011]
[9]张富利,崔德琪,王秋生,等.基于深度学习和图谱库方法自动勾画肿瘤放疗中危及器官的比较[J].中国医学物理学杂志,2019,36(12):1486.[doi:DOI:10.3969/j.issn.1005-202X.2019.12.024]
 ZHANG Fuli,CUI Deqi,WANG Qiusheng,et al.Comparative study of deep learning- versus Atlas-based auto-segmentation of organs-at-risk in tumor radiotherapy[J].Chinese Journal of Medical Physics,2019,36(6):1486.[doi:DOI:10.3969/j.issn.1005-202X.2019.12.024]
[10]温佳圆,林国钰,张逸文,等.应用深度学习网络实现肾小球滤过膜超微病理图像的语义分割[J].中国医学物理学杂志,2020,37(2):195.[doi:DOI:10.3969/j.issn.1005-202X.2020.02.012]
 WEN Jiayuan,LIN Guoyu,ZHANG Yiwen,et al.Semantic segmentation of ultrastructural pathological images of glomerular filtration membrane using deep learning network[J].Chinese Journal of Medical Physics,2020,37(6):195.[doi:DOI:10.3969/j.issn.1005-202X.2020.02.012]

备注/Memo

备注/Memo:
【收稿日期】2023-12-11 【基金项目】江苏省自然科学基金(BK20231337);江苏省高等学校自然科学研究项目(21KJD210004);南通市科技局基础科学研究项目(JC22022108, MSZ2022161, JCZ20173);南通理工学院中青年骨干教师项目(ZQNGGJS202237, ZQNGGJS202234) 【作者简介】杭益柳,硕士,讲师,研究方向:深度学习、数字图像处理,E-mail: 1194643361@qq.com 【通信作者】张琼,博士在读,讲师,研究方向:医学图像处理、数据挖掘,E-mail: 18862928127@163.com
更新日期/Last Update: 2024-06-25