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多分辨率融合输入的U型视网膜血管分割算法

梁礼明 詹涛 雷坤 冯骏 谭卢敏

梁礼明, 詹涛, 雷坤, 冯骏, 谭卢敏. 多分辨率融合输入的U型视网膜血管分割算法[J]. 电子与信息学报, 2023, 45(5): 1795-1806. doi: 10.11999/JEIT220470
引用本文: 梁礼明, 詹涛, 雷坤, 冯骏, 谭卢敏. 多分辨率融合输入的U型视网膜血管分割算法[J]. 电子与信息学报, 2023, 45(5): 1795-1806. doi: 10.11999/JEIT220470
LIANG Liming, ZHAN Tao, LEI Kun, FENG Jun, TAN Lumin. Multi-resolution Fusion Input U-shaped Retinal Vessel Segmentation Algorithm[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1795-1806. doi: 10.11999/JEIT220470
Citation: LIANG Liming, ZHAN Tao, LEI Kun, FENG Jun, TAN Lumin. Multi-resolution Fusion Input U-shaped Retinal Vessel Segmentation Algorithm[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1795-1806. doi: 10.11999/JEIT220470

多分辨率融合输入的U型视网膜血管分割算法

doi: 10.11999/JEIT220470
基金项目: 国家自然科学基金(51365017, 61463018),江西省自然科学基金面上项目(20192BAB205084),江西省教育厅科学技术研究重点项目(GJJ170491)
详细信息
    作者简介:

    梁礼明:男,教授,研究方向为机器学习、医学影像和系统建模等

    詹涛:男,硕士生,研究方向为医学图像分割

    雷坤:男,硕士生,研究方向为医学图像分割

    冯骏:男,硕士生,研究方向为医学图像分割

    谭卢敏:女,硕士,讲师,研究方向为医学影像

    通讯作者:

    谭卢敏 gztlm2017@163.com

  • 中图分类号: R318; TP391

Multi-resolution Fusion Input U-shaped Retinal Vessel Segmentation Algorithm

Funds: The National Natural Science Foundation of China (51365017, 61463018), The General Project of Jiangxi Provincial Natural Science Foundation (20192BAB205084), The Science and Technology Research Key Project, Department of Education, Jiangxi Province (GJJ170491)
  • 摘要: 针对视网膜血管拓扑结构不规则、形态复杂和尺度变化多样的特点,该文提出一种多分辨率融合输入的U型网络(MFIU-Net),旨在实现视网膜血管精准分割。设计以多分辨率融合输入为主干的粗略分割网络,生成高分辨率特征。采用改进的ResNeSt代替传统卷积,优化血管分割边界特征;将并行空间激活模块嵌入其中,捕获更多的语义和空间信息。构架另一U型精细分割网络,提高模型的微观表示和识别能力。一是底层采用多尺度密集特征金字塔模块提取血管的多尺度特征信息。二是利用特征自适应模块增强粗、细网络之间的特征融合,抑制不相关的背景噪声。三是设计面向细节的双重损失函数融合,以引导网络专注于学习特征。在眼底数据用于血管提取的数字视网膜图像(DRIVE)、视网膜结构分析(STARE)和儿童心脏与健康研究(CHASE_DB1)上进行实验,其准确率分别为97.00%, 97.47%和97.48%,灵敏度分别为82.73%, 82.86%和83.24%,曲线下的面积(AUC)值分别为98.74%, 98.90%和98.93%。其模型整体性能优于现有算法。
  • 图  1  MFIU-Net总体架构

    图  2  改进的ResNeSt模块

    图  3  并行空间激活模块

    图  4  多尺度密集特征金字塔模块

    图  5  各阶段预处理图像

    图  6  不同算法视网膜血管分割结果

    图  7  不同算法血管分割细节比较

    图  8  不同算法的ROC曲线

    图  9  不同算法的F1性能曲线

    表  1  对比实验结果

    $ {\lambda _1} $$ {\lambda _2} $AccSeSpF1AUC
    0.30.70.96910.79820.98680.82490.9865
    0.40.60.96890.81530.98360.82100.9850
    0.50.50.96960.78660.98720.81940.9859
    0.60.40.96880.82690.98250.82320.9860
    0.70.30.97000.82730.98360.82790.9874
    下载: 导出CSV

    表  2  不同算法的性能指标(%)

    方法DRIVESTARECHASE_DB1
    AccSeSpF1AUCAccSeSpF1AUCAccSeSpF1AUC
    文献[23]96.8475.4398.8980.6898.4397.2473.3399.2280.2998.4897.1970.7898.9676.0398.41
    文献[24]96.9579.0098.6881.9698.6597.3576.8099.0681.6398.6597.2172.7498.8676.6798.46
    文献[25]96.9778.2298.7881.9298.6697.3977.6199.0382.0198.7697.1972.0498.8876.3698.32
    文献[18]96.8578.1598.6581.3098.3797.2577.1898.9181.1298.5397.0878.0098.3677.1198.10
    文献[26]96.9479.5098.6282.0098.6497.3378.7098.8781.8698.7597.2277.3598.5677.8398.44
    文献[27]96.9581.5998.4382.4198.6997.3478.9298.8781.9898.7997.2381.1698.3078.6498.62
    MFIU-Net97.0082.7398.3682.7998.7497.4782.8698.6883.3798.9097.4883.2498.4380.5798.93
    下载: 导出CSV

    表  3  DRIVE数据集对比结果

    方法AccSeSpF1AUC
    文献[7]0.95680.81150.97800.82720.9810
    文献[8]0.96920.80270.98520.82060.9850
    文献9]0.95810.79910.98130.82930.9823
    文献[10]0.95650.78530.98180.82030.9834
    文献[26]0.95610.78560.98100.82020.9793
    文献[28]0.95660.79630.98000.82370.9802
    文献[29]0.95730.77350.98380.82050.9816
    文献[30]0.98580.79410.97980.82160.9847
    文献[31]0.95810.80460.98050.83030.9827
    MFIU-Net0.97000.82730.98360.82790.9874
    下载: 导出CSV

    表  4  STARE数据集对比结果

    方法AccSeSpF1AUC
    文献[8]0.97350.79510.98830.82160.9856
    文献[9]0.96730.81860.98440.83790.9881
    文献[10]0.96680.80020.98640.82890.9900
    文献[28]0.96410.75950.98780.81430.9832
    文献[29]0.97010.77150.98860.81460.9881
    文献[30]0.96400.75980.98780.81420.9824
    文献[31]0.96650.79140.98700.82760.9864
    MFIU-Net0.97470.82860.98680.83370.9890
    下载: 导出CSV

    表  5  CHASE_DB1数据集对比结果

    方法AccSeSpF1AUC
    文献[7]0.96640.80750.98410.82780.9872
    文献[9]0.96700.82390.98130.81910.9871
    文献[10]0.96670.81320.98400.82930.9893
    文献[26]0.96560.79780.98180.80310.9839
    文献[28]0.96100.81550.97520.78830.9804
    文献[29]0.96550.79700.98230.80730.9851
    文献[30]0.96080.81760.97040.78920.9865
    文献[31]0.96730.84020.98010.82480.9874
    TP0.97480.83240.98430.80570.9893
    下载: 导出CSV

    表  6  消融实验分析(%)

    方法DRIVESTARECHASE_DB1
    AccSeSpF1AUCAccSeSpF1AUCAccSeSpF1AUC
    M196.8475.4398.8980.6898.4097.2473.3399.2280.2998.5397.1970.7898.9676.0398.41
    M296.8977.8898.7281.4498.5697.3575.1199.2081.3098.7597.2475.2698.7277.4798.43
    M396.9180.0998.5782.1398.5897.3877.9098.9981.9898.6997.3079.7698.4878.8298.65
    M496.9580.4398.5382.1898.6197.4080.8898.7782.6698.8597.4181.4398.4979.8998.83
    M596.9981.6998.4782.6698.6797.4381.5298.7482.9198.8897.4581.8498.5080.1998.87
    M697.0082.7398.3682.7998.7497.4782.8698.6883.3798.9097.4883.2498.4380.5798.93
    下载: 导出CSV
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  • 收稿日期:  2022-04-18
  • 修回日期:  2022-07-13
  • 网络出版日期:  2022-07-19
  • 刊出日期:  2023-05-10

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