融合加权随机森林的自动3D椎骨CT图像主动轮廓分割方法

刘侠,甘权,李冰,等. 融合加权随机森林的自动3D椎骨CT图像主动轮廓分割方法[J]. 光电工程,2020,47(12):200002. doi: 10.12086/oee.2020.200002
引用本文: 刘侠,甘权,李冰,等. 融合加权随机森林的自动3D椎骨CT图像主动轮廓分割方法[J]. 光电工程,2020,47(12):200002. doi: 10.12086/oee.2020.200002
Liu X, Gan Q, Li B, et al. Automatic 3D vertebrae CT image active contour segmentation method based on weighted random forest[J]. Opto-Electron Eng, 2020, 47(12): 200002. doi: 10.12086/oee.2020.200002
Citation: Liu X, Gan Q, Li B, et al. Automatic 3D vertebrae CT image active contour segmentation method based on weighted random forest[J]. Opto-Electron Eng, 2020, 47(12): 200002. doi: 10.12086/oee.2020.200002

融合加权随机森林的自动3D椎骨CT图像主动轮廓分割方法

  • 基金项目:
    国家自然科学基金资助项目(61172167);黑龙江省自然科学基金资助项目(QC2017076)
详细信息
    作者简介:
    通讯作者: 王波(1982-),男,博士,副教授,硕士生导师,主要从事模式识别,机器学习,医学影像分析与处理,自然语言处理等的研究。E-mail:hust_wb@126.com
  • 中图分类号: TP391.4

Automatic 3D vertebrae CT image active contour segmentation method based on weighted random forest

  • Fund Project: Supported by National Natural Science Foundation of China (61172167) and Natural Science Foundation of Heilongjiang Province (QC2017076)
More Information
  • 为了解决CT图像主动轮廓分割方法对初始轮廓的敏感和分割不准确的问题,本文提出一种融合加权随机森林的自动3D椎骨CT主动轮廓分割方法WRF-AC。该方法提出加权随机森林算法和包含边缘能量的主动轮廓能量函数。首先,通过提取椎骨CT的3D Haar-like特征值训练加权随机森林获得的椎骨中心作为分割的初始轮廓,然后,求解包含边缘能量的主动轮廓能量函数最小值完成椎骨CT图像的分割。实验结果表明,本方法在相同数据集上能够更加准确、快速地分割脊柱CT图像提取椎骨部分。

  • Overview: Medical image segmentation has been widely used in medical image diagnosis technology and has become one of the indispensable means of clinical treatment. The use of computer processing to analyze spine CT images in modern medicine has become an important research direction, and has very important clinical application values. Due to the complicated structure of the vertebral body and the small difference, it is difficult for people to accurately extract the vertebral body of interest. In previous studies, we tried to manually set the initial contour directly to construct an interactive semi-automatic segmentation scheme. However, due to a large number of vertebrae in the human spine and the similar shape of the vertebrae, the manual setting of initial contour points requires a certain medical foundation and consumes much time. In order to solve the problems of sensitive initial contours and inaccurate segmentation caused by active contour segmentation of CT images, this paper proposes an automatic 3D vertebral CT active contour segmentation method combined weighted random forest called "WRF-AC". This method proposes a weighted random forest algorithm and an active contour energy function that includes edge energy. First, the weighted random forest is trained by extracting 3D Haar-like feature values of the vertebra CT, and the 'vertebra center' obtained is used as the initial contour of the segmentation. Then, the segmentation of the vertebra CT image is completed by solving the active contour energy function minimum containing the edge energy. The experimental results show that this method can segment the spine CT images more accurately and quickly on the same datasets to extract the vertebrae. Experimental results show that the average segmentation accuracy of the active contour segmentation method of 3D vertebra CT image fusion weighted random forest proposed in this paper can reach more than 92%. This method has certain advantages: it can automatically locate the center of the vertebrae and accurately segment the vertebral area; it is easy to obtain CT images of the spine, using the segmentation model proposed in this paper to segment the vertebral area, and combining the subsequent 3D reconstruction and 3D printing can easily help clinical applications and treatment. Due to the difficulty in collecting CT data of vertebrae, it is necessary to add more segmentation data for model training in the subsequent research to improve the segmentation accuracy of the segmentation model and achieve multi-level segmentation of the spine.

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  • 图 1  本文的流程图

    Figure 1.  Flowchart for this article

    图 2  随机森林中心点定位。(a)三维距离图;(b)回归森林得到的回归点

    Figure 2.  Random forest center point positioning. (a) 3D distance map; (b) Regression points obtained by regression forest

    图 3  椎骨中心及初始轮廓

    Figure 3.  Vertebral center and initial contours

    图 4  控制变量实验分割结果

    Figure 4.  Controlled variable experimental segmentation results

    图 5  分析实验分割结果

    Figure 5.  Analysis of experimental segmentation results

    图 6  DC和ASD统计结果

    Figure 6.  DC and ASD statistical results

    图 7  20例DC系数和ASD系数

    Figure 7.  DC coefficients and ASD coefficients of 20 cases

    图 8  方法分割结果

    Figure 8.  Segmentation results of the proposed method

    图 9  本文方法与其他方法之间分割效果的比较

    Figure 9.  Comparisons of the segmentation effect between the proposed method and other methods

    图 10  椎骨三维重建可视化效果

    Figure 10.  Visualization of 3D reconstruction of vertebrae

    表 1  定量实验分割结果

    Table 1.  Segmentation results of quantitative experiments

    指数 任意初始轮廓 边缘能量 本文方法
    无边缘能量 有边缘能量 无RF初始轮廓 有RF初始轮廓 WRF-EAC
    DC 0.65 0.78 0.83 0.935 0.954
    ASD 4.32 3.15 1.26 0.462 0.306
    CCR 0.66 0.78 0.85 0.938 0.955
    Jaccard 0.63 0.75 0.80 0.930 0.952
    Time cost 45 min per case 40 min per case 20 min per case 15 min per case 13 min per case
    下载: 导出CSV

    表 2  分割结果各评价指标

    Table 2.  Segmentation results of each evaluation index

    脊柱区段 DC ASD CCR Jaccard
    Health case Unhealth case Health case Unhealth case Health case Unhealth case Health case Unhealth case
    T1~T6 0.922 0.854 0.475 3.715 0.924 0.847 0.918 0.861
    T7~T12 0.953 0.917 0.452 0.832 0.954 0.915 0.949 0.902
    L1~L5 0.968 0.939 0.305 0.373 0.967 0.938 0.957 0.936
    All 0.955 0.925 0.353 0.405 0.954 0.921 0.953 0.927
    下载: 导出CSV

    表 3  分割结果的DC系数比较

    Table 3.  Comparisons of DC coefficients for segmentation results

    方法 椎骨定位 分割方法 Dice 耗时
    1[25] 自动 Multi-atlas 0.93 12 min/case
    2[26] 手动 Mean shape 0.93 45 min/case
    3[27] 手动 Mean shape 0.931 3 min/case
    4[28] 自动 CNN 0.947 27 s/piece
    本文方法 自动 WRF-FEAC 0.951 13 min/case
    下载: 导出CSV
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出版历程
收稿日期:  2020-01-02
修回日期:  2020-04-16
刊出日期:  2020-12-15

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