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基于循环显著性校准网络的胰腺分割方法

邱成健 刘青山 宋余庆 刘哲

邱成健, 刘青山, 宋余庆, 刘哲. 基于循环显著性校准网络的胰腺分割方法. 自动化学报, 2022, 48(11): 2703−2717 doi: 10.16383/j.aas.c210865
引用本文: 邱成健, 刘青山, 宋余庆, 刘哲. 基于循环显著性校准网络的胰腺分割方法. 自动化学报, 2022, 48(11): 2703−2717 doi: 10.16383/j.aas.c210865
Qiu Cheng-Jian, Liu Qing-Shan, Song Yu-Qing, Liu Zhe. Pancreas segmentation based on recurrent saliency calibration network. Acta Automatica Sinica, 2022, 48(11): 2703−2717 doi: 10.16383/j.aas.c210865
Citation: Qiu Cheng-Jian, Liu Qing-Shan, Song Yu-Qing, Liu Zhe. Pancreas segmentation based on recurrent saliency calibration network. Acta Automatica Sinica, 2022, 48(11): 2703−2717 doi: 10.16383/j.aas.c210865

基于循环显著性校准网络的胰腺分割方法

doi: 10.16383/j.aas.c210865
基金项目: 国家自然科学基金(61976106, 61772242, 61572239), 中国博士后科学基金(2017M611737), 江苏省六大人才高峰计划(DZXX-122), 江苏省研究生科研创新计划(KYCX21_3374)资助
详细信息
    作者简介:

    邱成健:江苏大学计算机科学与通信工程学院博士博士研究生. 主要研究方向为医学图像分割. E-mail: 2111908005@stmail.ujs.edu.cn

    刘青山:南京信息工程大学自动化学院教授. 主要研究方向为视频内容分析与理解. E-mail: qsliu@nuist.edu.cn

    宋余庆:江苏大学计算机科学与通信工程学院教授. 主要研究方向为医学图像分析, 数据挖掘. E-mail: yqsong@ujs.edu.cn

    刘哲:江苏大学计算机科学与通信工程学院教授. 主要研究方向为数据智能处理, 医学图像分析. 本文通信作者. E-mail: lzhe@ujs.edu.cn

Pancreas Segmentation Based on Recurrent Saliency Calibration Network

Funds: Supported by National Natural Science Foundation of China (61976106, 61772242, 61572239), China Postdoctoral Science Foundation (2017M611737), Six Talent Peaks Project in Jiangsu Province (DZXX-122), and Graduate Student Scientific Research Innovation Projects in Jiangsu Province (KYCX21_3374)
More Information
    Author Bio:

    QIU Cheng-Jian Ph.D. candidate at the School of Computer Science and Communication Engineering, Jiangsu University. His main research interest is medical image segmentation

    LIU Qing-Shan Professor at the School of Automation, Nanjing University of Information Science and Technology. His main research interest is video content analysis and understanding

    SONG Yu-Qing Professor at the School of Computer Science and Communication Engineering, Jiangsu University. His research interest covers medical image analysis and data mining

    LIU Zhe Professor at the School of Computer Science and Communication Engineering, Jiangsu University. Her research interest covers intelligent data processing and medical image analysis. Corresponding author of this paper

  • 摘要: 胰腺的准确分割对于胰腺癌的识别和分析至关重要. 研究者提出通过第一阶段粗分割掩码的位置信息缩小第二阶段细分割网络输入的由粗到细分割方法, 尽管极大地提升了分割精度, 但是在胰腺分割过程中对于上下文信息的利用却存在以下两个问题: 1) 粗分割和细分割阶段分开训练, 细分割阶段缺少粗分割阶段分割掩码信息, 抑制了阶段间上下文信息的流动, 导致部分细分割阶段结果无法比粗分割阶段更准确; 2) 粗分割和细分割阶段单批次相邻预测分割掩码之间缺少信息互监督, 丢失切片上下文信息, 增加了误分割风险. 针对上述问题, 提出了一种基于循环显著性校准网络的胰腺分割方法. 通过循环使用前一阶段输出的胰腺分割掩码作为当前阶段输入的空间权重, 进行两阶段联合训练, 实现阶段间上下文信息的有效利用; 提出卷积自注意力校准模块进行胰腺预测分割掩码切片上下文信息跨顺序互监督, 显著改善了相邻切片误分割现象. 提出的方法在公开的数据集上进行了验证, 实验结果表明其改善误分割结果的同时提升了平均分割精度.
  • 图  1  粗细分割存在问题示例

    Fig.  1  A failure case of the coarse-to-fine pancreas segmentation approach

    图  2  误分割示例

    Fig.  2  An example of false segmentation

    图  3  循环显著性校准网络

    Fig.  3  Recurrent saliency calibration network

    图  4  迭代过程

    Fig.  4  Iteration process

    图  5  基于最小矩形框的定位过程

    Fig.  5  The process of localization based on minimum rectangle algorithm

    图  6  卷积自注意力校准模块网络图

    Fig.  6  Network of convolution self-attention calibration module

    图  7  本文方法在NIH数据集及MSD数据集上箱线图

    Fig.  7  Box plot of the method in this paper on NIH dataset and MSD dataset

    图  8  NIH数据集分割结果对比

    Fig.  8  Comparison of segmentation results on NIH dataset

    图  9  MSD数据集分割结果对比

    Fig.  9  Comparison of segmentation results on MSD dataset

    表  1  粗细分割分开训练、联合训练和循环显著性联合训练分割结果

    Table  1  Segmentation of coarse-to-fine separate training, joint training and recurrent saliency joint training

    方法平均 DSC (%) ± Std (%)最大 DSC (%)最小 DSC (%)
    NIHMSDNIHMSDNIHMSD
    粗细分割分开训练$81.96 \pm 5.79$$78.92 \pm 9.61$89.5889.9148.3951.23
    粗细分割联合训练$83.08 \pm 5.47$$80.80 \pm 8.79$90.5891.1349.9452.79
    循环显著性网络联合训练$85.56 \pm 4.79$$83.24 \pm 5.93$91.1492.8062.8264.47
    下载: 导出CSV

    表  2  循环显著性网络测试结果

    Table  2  Test results of recurrent saliency network segmentation

    迭代次数平均 DSC (%) ± Std (%)最大 DSC (%)最小 DSC (%)
    NIHMSDNIHMSDNIHMSD
    第 0 次迭代 (粗分割)$76.81 \pm 9.68$$73.46 \pm 11.73$87.9488.6740.1247.76
    第 1 次迭代$84.89 \pm 5.14$$81.67 \pm 8.05$91.0291.8950.3652.90
    第 2 次迭代$83.34\pm 5.07$$82.23 \pm 7.57$90.9691.9453.7356.81
    第 3 次迭代$85.63 \pm 4.96$$82.78 \pm 6.83$91.0892.3257.9658.04
    第 4 次迭代$85.79 \pm 4.83$$82.94 \pm 6.46$91.1592.5662.9763.73
    第 5 次迭代$85.82 \pm 4.82$$83.15 \pm 6.04$91.2092.7762.8563.99
    第 6 次迭代$85.86 \pm 4.79$$83.24 \pm 5.93$91.1492.8062.8264.47
    下载: 导出CSV

    表  3  添加校准模块结果对比

    Table  3  Comparison results of adding calibration module

    方法平均 DSC (%) ± Std (%)最大 DSC (%)最小 DSC (%)
    NIHMSDNIHMSDNIHMSD
    粗细分割联合训练未添加校准模块$83.08 \pm 5.47$$80.80 \pm 8.79$90.5891.1349.9452.79
    粗细分割联合训练添加校准模块$84.72 \pm 5.07$$82.09 \pm 7.91$90.9892.9050.2753.35
    循环显著性网络未添加校准模块$85.86 \pm 4.79$$83.24 \pm 5.93$91.1492.8062.8264.47
    循环显著性网络添加校准模块$87.11 \pm 4.02$$85.13 \pm 5.17$92.5794.4867.3068.24
    下载: 导出CSV

    表  4  胰腺分割基于CLSTM和自注意力结果对比

    Table  4  Comparison results based on CLSTM and self-attention mechanism in pancreas segmentation

    方法平均 DSC (%) ± Std (%)最大 DSC (%)最小 DSC (%)
    NIHMSDNIHMSDNIHMSD
    基于 CLSTM 校准模块$86.13 \pm 4.54$$84.21 \pm 5.80$91.2093.4763.1864.76
    基于 ConvGRU 校准模块$86.34 \pm 4.21$$84.41\pm 5.62$92.3194.0565.7366.02
    基于 TrajGRU 校准模块$86.96 \pm 4.14$$84.87 \pm 5.22$92.4994.3267.2067.93
    基于卷积自注意力校准模块$ 87.11 \pm 4.02$$85.13 \pm 5.17$92.5794.4867.3068.24
    下载: 导出CSV

    表  5  NIH数据集上不同分割方法结果比较(“—”表示文献中缺少参数说明)

    Table  5  Comparison of different segmentation methods on NIH dataset (“—” indicates a lack of reference in the literature)

    方法分割维度平均 DSC (%) ±
    Std (%)
    最大 DSC (%)最小 DSC (%)
    文献 [22]2D$71.80 \pm 10.70$86.9025.00
    文献 [23]2D$81.27 \pm 6.27$88.9650.69
    文献 [36]2D$82.40 \pm 6.70$90.1060.00
    文献 [3]2D$82.37 \pm 5.68$90.8562.43
    文献 [37]3D$84.59 \pm 4.86$91.4569.62
    文献 [10]3D$85.99 \pm 4.51$91.2057.20
    文献 [5]3D$85.93 \pm 3.42$91.4875.01
    文献 [29]3D$82.47 \pm 5.50$91.1762.36
    文献 [20]2D$82.87 \pm 1.00$87.6781.18
    文献 [19]2D$84.90 \pm -$91.4661.82
    文献 [26]2D$85.35 \pm 4.13$91.0571.36
    文献 [21]2D$85.40 \pm 1.60$
    文献 [30]3D$86.19 \pm -$91.9069.17
    本文方法2D87.11 ± 4.0292.5767.30
    下载: 导出CSV

    表  6  MSD数据集上不同分割方法结果比较

    Table  6  Comparison of different segmentation methods on MSD dataset

    方法分割维度平均 DSC (%) ±
    Std (%)
    最大 DSC (%)最小 DSC (%)
    文献 [39]3D$79.98\pm7.71$93.7361.64
    文献 [38]3D$82.37\pm5.68$90.8562.43
    文献 [28]2D$84.71\pm7.13$95.5458.62
    文献 [40]3D$84.22\pm5.91$92.7566.58
    本文方法2D85.13 ± 5.1794.4868.24
    下载: 导出CSV

    表  7  NIH数据集不同网络输入切片数目分割结果比较

    Table  7  Comparison of the segmentation of different network input slices on NIH dataset

    网络输入
    切片数目
    分割维度平均 DSC (%) ±
    Std (%)
    最大 DSC (%)最小 DSC (%)
    32D$87.11\pm4.02$92.5767.30
    52D$87.53\pm3.74$92.6969.32
    72D$87.96\pm3.25$92.9471.91
    下载: 导出CSV

    表  8  MSD数据集不同网络输入切片数目分割结果比较

    Table  8  Comparison of the segmentation of different network input slices on MSD dataset

    网络输入
    切片数目
    分割维度平均 DSC (%) ±
    Std (%)
    最大 DSC (%)最小 DSC (%)
    32D$85.13\pm5.17$94.4868.24
    52D$85.86\pm5.01$94.7570.31
    72D$86.29\pm4.80$95.0173.07
    下载: 导出CSV

    表  9  不同分割方法参数量比较

    Table  9  Comparison of the number of parameters of different segmentation methods

    方法分割维度参数量
    FCN[2]2D134.26 M
    UNet[15]2D28.34 M
    3D UNet[41]3D16.31 M
    AttentionUNet[42]2D35.06 M
    UNet++ [43]2D36.74 M
    Fix-point[3]2D807.93 M
    GGPFN[28]2D + 3D42.00 M
    本文方法2D59.47 M
    下载: 导出CSV

    表  10  不同分割方法时间消耗比较(“—”表示文献中缺少参数说明)

    Table  10  Comparison of time consumption of different segmentation methods (“—” indicates a lack of reference in the literature)

    方法分割维度每个病例平均
    测试时间
    (min)
    训练时间
    (h)
    设备
    文献 [44]2D$2\sim 3$
    文献 [22]2D$1\sim 3$$\sim 55$GTX Titan Z (12 GB)
    文献 [23]2D$2\sim 3$$9\sim12$Titan X (12 GB)
    文献 [3]2D$\sim 3$
    文献 [36]2D$\sim 3$GTX Titan X (12 GB)
    本文方法2D1.1$\sim 8$RTX 2080ti (11 GB)
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-09-09
  • 录用日期:  2022-03-13
  • 网络出版日期:  2022-05-04
  • 刊出日期:  2022-11-22

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