[1]曲小波,余粟.改进U-Net深度网络的视网膜血管分割算法[J].中国医学物理学杂志,2023,40(10):1212-1219.[doi:DOI:10.3969/j.issn.1005-202X.2023.10.004]
　QU Xiaobo,YU Su.Retinal blood vessel segmentation algorithm based on improved U-Net[J].Chinese Journal of Medical Physics,2023,40(10):1212-1219.[doi:DOI:10.3969/j.issn.1005-202X.2023.10.004]
点击复制

改进U-Net深度网络的视网膜血管分割算法()
分享到：

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

卷:
40卷
期数:
2023年第10期
页码:
1212-1219
栏目:
医学影像物理
出版日期:
2023-10-27

文章信息/Info

Title:
Retinal blood vessel segmentation algorithm based on improved U-Net
文章编号:
1005-202X（2023）10-1212-08
作者:
曲小波1; 余粟2
1.上海工程技术大学电子电气工程学院， 上海 201620； 2.上海工程技术大学工程实训中心， 上海 201620
Author(s):
QU Xiaobo1;  YU Su2
1. College of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China 2. Engineering Training Center, Shanghai University of Engineering Science, Shanghai 201620, China
关键词:
医疗图像分割; 深度学习; 通道强化残差网络; 空间注意力网络; 动态损失函数
Keywords:
Keywords: medical image segmentation deep learning channel enhancement residual network spatial attention network dynamic loss function
分类号:
R318；TP391
DOI:
DOI:10.3969/j.issn.1005-202X.2023.10.004
文献标志码:
A
摘要:
为了解决U-Net算法在分割眼底图像时无法分割末梢微小血管和无法处理噪声干扰等问题，提出了一种改进的视网膜血管分割算法。首先，在U-Net算法中引入通道强化残差网络，用以优化U-Net架构，使得网络识别更多视网膜微血管。其次，引入空间注意力网络来排除噪声，更好地突出血管。最后，在损失函数的计算中，使用动态权重代替U-Net算法的固定权重，迫使神经网络能够学习一个稳健的特征映射。将改进的算法在DRIVE数据集上进行实验，实验结果表明本文分割算法的准确性和敏感性大幅提高。比原U-Net算法准确性和敏感性分别提高了2.12%和7.51%，比DCU-Net准确性和敏感性分别提高了1.20%和2.55%。
Abstract:
Abstract: An improved retinal blood vessel segmentation algorithm is proposed to address the problem that U-Net algorithm can not segment the tiny peripheral blood vessels and deal with noise interference in fundus image segmentation. The proposed method introduces channel enhancement residual network into U-Net algorithm to optimize the U-Net architecture and make the network recognize more retinal microvessels, uses spatial attention network to eliminate noise and better highlight blood vessels, and replaces the fixed weight of U-Net algorithm with dynamic weight in the calculation of the loss function for enabling the neural network to learn a robust feature map. The experiment on DRIVE dataset show that the improved algorithm exhibits better performance of accuracy and sensitivity, which are 2.12% and 7.51% higher than the original U-Net algorithm, and 1.20% and 2.55% higher than DCU-Net algorithm.

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(10):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(10):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(10):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(10):1307.[doi:DOI:10.3969/j.issn.1005-202X.2018.11.013]
[5]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(10):420.[doi:DOI:10.3969/j.issn.1005-202X.2019.04.010]
[6]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(10):1103.[doi:DOI:10.3969/j.issn.1005-202X.2019.09.021]
[7]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(10):1302.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.011]
[8]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(10):1486.[doi:DOI:10.3969/j.issn.1005-202X.2019.12.024]
[9]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(10):195.[doi:DOI:10.3969/j.issn.1005-202X.2020.02.012]
[10]秦楠楠,薛旭东,吴爱林,等.基于U-net卷积神经网络的宫颈癌临床靶区和危及器官自动勾画的研究[J].中国医学物理学杂志,2020,37(4):524.[doi:DOI:10.3969/j.issn.1005-202X.2020.04.023]
　QIN Nannan,XUE Xudong,WU Ailin,et al.Automatic segmentation of clinical target volumes and organs-at-risk in radiotherapy for cervical cancer using U-net convolutional neural network[J].Chinese Journal of Medical Physics,2020,37(10):524.[doi:DOI:10.3969/j.issn.1005-202X.2020.04.023]

备注/Memo

备注/Memo:
【收稿日期】2023-04-24 【基金项目】国家科技部“十二五”支撑计划（2015BAF10B00） 【作者简介】曲小波，硕士研究生，研究方向：图像分割，E-mail: 429655770@qq.com 【通信作者】余粟，硕士，硕士生导师，教授，研究方向：计算机科学、深度学习与图像识别，E-mail: yusu@sues.edu.cn
更新日期/Last Update: 2023-10-27