Automatic 3D vertebrae CT image active contour segmentation method based on weighted random forest
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摘要:
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。
Abstract: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.
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Key words:
- 3D segmentation /
- CT images /
- weighted random forest /
- active contour
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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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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 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 -
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