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基于特征图金字塔的冠脉造影图像血管分割方法

郭昊虎 高若谦 葛明锋 董文飞 刘炎 赵旭峰

郭昊虎, 高若谦, 葛明锋, 董文飞, 刘炎, 赵旭峰. 基于特征图金字塔的冠脉造影图像血管分割方法[J]. 中国光学(中英文), 2024, 17(4): 971-981. doi: 10.37188/CO.2023-0186
引用本文: 郭昊虎, 高若谦, 葛明锋, 董文飞, 刘炎, 赵旭峰. 基于特征图金字塔的冠脉造影图像血管分割方法[J]. 中国光学(中英文), 2024, 17(4): 971-981. doi: 10.37188/CO.2023-0186
GUO Hao-hu, GAO Ruo-qian, GE Ming-feng, DONG Wen-fei, LIU Yan, ZHAO Xu-feng. Coronary artery angiography image vessel segmentation method based on feature pyramid network[J]. Chinese Optics, 2024, 17(4): 971-981. doi: 10.37188/CO.2023-0186
Citation: GUO Hao-hu, GAO Ruo-qian, GE Ming-feng, DONG Wen-fei, LIU Yan, ZHAO Xu-feng. Coronary artery angiography image vessel segmentation method based on feature pyramid network[J]. Chinese Optics, 2024, 17(4): 971-981. doi: 10.37188/CO.2023-0186

基于特征图金字塔的冠脉造影图像血管分割方法

cstr: 32171.14.CO.2023-0186
基金项目: 国家重点研发计划(No. 2021YFC2500500);吉林省与中国科学院科技合作高新技术产业化专项资金项目(No. 2023SYHZ0037)
详细信息
    作者简介:

    高若谦(1993—),男,吉林长春人,2020年于中国科学院长春光学精密机械与物理研究所获得博士学位,主要从事高光谱、成像光学等方面研究。E-mail:gaorq@sibet.ac.cn

  • 中图分类号: TP391.41;

Coronary artery angiography image vessel segmentation method based on feature pyramid network

Funds: Supported by the National Key R&D Program of China (No. 2021YFC2500500); Science and Technology Cooperation Special Project, Jilin Province and Chinese Academy of Sciences (No. 2023SYHZ0037)
More Information
  • 摘要:

    针对冠脉造影图像照明不均、血管结构与背景区域对比度低、冠脉血管拓扑结构复杂等分割难点,建立了一个冠脉造影血管分割标注数据集,并在此基础上提出了一种基于特征图金字塔的冠脉造影图像血管分割模型。本文模型以U-Net网络为基础进行改进和优化,首先,将U-Net编码部分的第一个卷积层修改为一个7×7的卷积层,并提高每一层的感受野,在编解码层中引入修改后的ConvNeXt block,使得网络提取更深层次特征的能力有所提升;其次,设计分组注意力机制模块GA,并将其引入到U-Net跨连接处,对编码部分提取的特征进行增强,弥补编解码器间存在的语义差距;最后,在U-Net解码器处设计了一个特征图金字塔级联模块PFC,融合各尺度的特征图,并在PFC中每一层中加入SE注意力机制模块,用于筛选特征图中的有效信息,网络损失函数为PFC模块各层输出的加权,以监督网络各层的特征提取。本文模型在测试集上的测试结果如下:Dice系数为0.8843,Jaccard系数为0.7926。实验结果表明,相比其他常用方法,本文模型在冠脉血管分割上具有较强的鲁棒性,在低对比度下能够有效抑制噪声,对冠脉血管具有更好的分割效果。

     

  • 图 1  模型网络结构

    Figure 1.  Network structure of the proposed model

    图 2  ConvNeXt模块

    Figure 2.  ConvNeXt block

    图 3  修改后的ConvNeXt模块

    Figure 3.  Modified ConvNeXt block

    图 4  特征金字塔网络

    Figure 4.  Feature Pyramid Network

    图 5  金字塔特征级联(PFC)

    Figure 5.  Pyramid Feature Concatenation(PFC)

    图 6  SE注意力机制

    Figure 6.  SE attention mechanism

    图 7  分组注意力机制模块

    Figure 7.  Group Attention block

    图 8  验证集上不同算法的Dice曲线

    Figure 8.  Dice curves of different algorithms on the testing set

    图 9  不同算法分割效果对比图

    Figure 9.  Comparison of segmentation effects with different algorithm

    表  1  各模块对性能的影响

    Table  1.   Each module’s impact on performance

    网络JaccardDice
    BaseNet0.6476±0.00980.7861±0.0073
    BaseNet+Conv.7×70.6606±0.01190.7956±0.0086
    BaseNet+ConvNeXt block0.7104±0.00650.8307±0.0044
    BaseNet+修改后的 ConvNeXt block0.7234±0.00440.8395±0.0030
    BaseNet+PFC0.7036±0.00880.8260±0.0061
    BaseNet+PFC+SE0.7104±0.00430.8260±0.0061
    BaseNet+PFC+SE+加权Loss0.7364±0.00620.8482±0.0041
    BaseNet+GA0.7044±0.00280.8266±0.0019
    BaseNet+Conv.7×7 +PFC+SE +修改后的ConvNeXt block+GA+加权Loss0.7926±0.00580.8843±0.0036
    下载: 导出CSV

    表  2  不同算法测试结果

    Table  2.   Test results for different algorithms

    网络JaccardDiceAUCAccuracyPrecisionSensitivitySpecificity
    U-Net0.71160.83150.97330.97060.87690.79080.9888
    ResUNeXt0.68490.81300.87410.96830.88470.75260.9901
    TransUnet0.74210.85190.98840.97480.86280.84160.9873
    MultiResUnet0.76640.86770.98420.97610.87750.85830.9879
    Ours0.79260.88430.99130.97830.90080.85970.9904
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-10-21
  • 修回日期:  2023-12-05
  • 网络出版日期:  2024-05-09

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