Automatic Liver Segmentation From CT Volumes Based on Level Set and Shape Descriptor
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摘要: 肝脏分割是计算机辅助肝脏疾病诊断的重要前提和基础.本文提出了一种新的基于水平集和形状描述符的腹部CT序列图像肝脏自动分割方法.首先, 对原始腹部CT序列图像进行预处理, 去除与肝脏不相关的器官和组织.然后, 利用灰度偏移场, 结合周长项、距离正则项和相邻切片肝脏分割结果构建水平集能量函数, 实现CT序列肝脏自动分割.为避免分割误差累积, 提出一种基于形状描述符和瓶颈率的肝脏边缘优化方法, 在每张切片分割完毕后去除由于灰度重叠造成的过分割.通过对XHCSU14数据库和Sliver07数据库中腹部CT序列的肝脏分割实验, 以及与其他肝脏分割算法的比较, 表明了本文方法的有效性, 且分割精度高, 鲁棒性强.Abstract: Liver segmentation is an important prerequisite and basis for computer-assisted liver disease diagnosis. This paper proposes a novel method for automatic liver segmentation from CT volume based on level set and shape descriptor. First, irrelevant tissues and organs are removed from original CT volume. Then, intensity bias field together with perimeter term, distance regular term, and segmentation result of neighbor slice is utilized to construct a level set energy function, through which initial liver segmentation results for the CT volume are generated automatically. To avoid segmentation error accumulation, a liver boundary refinement method based on shape descriptor and bottleneck rate is proposed for each initial segmented slice to remove over-segmentation regions caused by intensity overlap. The experiments on CT volumes from XHCSU14 and Sliver07 databases, as well as the comparison with other algorithms show that our method can segment livers effectively with high accuracy and robustness.
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Key words:
- Liver segmentation /
- level set /
- shape descriptor /
- abdominal CT image
1) 本文责任编委 张道强 -
图 2 肝脏CT图像预处理.第一行:原始腹部CT图像; 第二行:预处理结果图像
Fig. 2 Pre-processing for liver CT image. First row: Original CT images; Second row: Results of pre-processing
图 3 基于偏移场的灰度校正. (a)预处理后的腹部CT图像; (b)位置约束掩模中的肝脏切片; (c)偏移场; (d)偏移场校正后的切片图像
Fig. 3 Bias field based intensity correction. (a) Pre-processed abdominal CT images; (b) Liver slices within masks generated by location constraints; (c) Bias fields; (d) Liver slices corrected by bias fields
图 4 基于水平集的肝脏粗分割. (a)预处理后的腹部CT原始图像; (b)水平集演化初始轮廓(白色曲线); (c)肝脏粗分割结果(白色曲线)
Fig. 4 Initial liver segmentation based on level set. (a) Preprocessed CT image; (b) Initial contour of level set evolution (white curve); (c) Initial segmentation result (white curve)
图 6 肝脏边缘优化. (a)过分割候选关键点(白色点); (b)过分割关键点对(白色点); (c)边缘优化结果
Fig. 6 Liver boundary refinement. (a) Candidate points of over-segmented region (white points); (b) Right key points of over-segmented region (white points); (c) The refined liver boundary
图 7 基于形状描述符和瓶颈率的边缘优化.第一行:肝脏粗分割结果; 第二行:肝脏边缘优化结果
Fig. 7 Examples of boundary refinement based on shape descriptor and bottleneck rate. First row: Initial liver segmentation results; Second row: Results of liver boundary refinement
图 8 部分切片肝脏分割结果.第一行: XHCSU14数据库肝脏分割结果; 第三行: Sliver07数据库肝脏分割果; 第二和第四行:肝脏分割果局部放大图.(白色曲线表示专家标记肝脏区域, 黑色曲线表示本文算法肝脏分割结果)
Fig. 8 Some examples of liver segmentation results. First and third rows: Examples of liver segmentation results on XHCSU14 and Sliver07 databases, respectively; Second and fourth rows: Partial enlarged liver segmentation results(The white and black curves are segmentation results of experts and the proposed method, respectively)
图 9 XHCSU14数据库肝脏分割结果的FPR和FNR分布图. (a)FPR分布图; (b) FNR分布图
Fig. 9 FPR and FNR distributions of segmentation results for XHCSU14 database. (a) FPR distribution; (b) FNR distribution
图 10 Sliver07数据库肝脏分割结果的FPR和FNR分布图. (a) FPR分布图; (b) FNR分布图
Fig. 10 FPR and FNR distributions of segmentation results for Sliver07 database. (a) FPR distribution; (b) FNR distribution
图 11 XHCSU14数据库和Sliver07数据库Dice系数分布图. (a) XHCSU14数据库肝脏分割结果Dice系数分布图; (b) Sliver07数据库肝脏分割结果Dice系数分布图
Fig. 11 Dice coefficients distributions for XHCSU14 and Sliver07 databases, respectively. (a) The Dice similar coefficients distribution of XHCSU14 database; (b) The Dice similar coefficients distribution of Sliver07 database
图 12 Sliver07数据库病变肝脏切片分割结果.黑色曲线表示本文算法分割结果, 白色曲线表示专家手工标记结果
Fig. 12 Liver segmentation results of slices with severe hepatic lesions. The black and white curve represents segmentation results by the proposed method and experts, respectively
表 1 XHCSU14数据库分割性能比较(均值$\pm $标准差)
Table 1 Segmentation performance comparison on XHCSU14 database (mean $\pm$ std)
方法 VOE (%) RVD (%) ASD (mm) RMSD (mm) MSD (mm) 文献[4] 8.1$\pm $1.6 5.4$\pm $3.7 1.3$\pm $0.3 2.8$\pm $0.9 42.5$\pm $18.0 文献[6] 10.2$\pm $2.1 2.6$\pm $2.4 1.5$\pm $0.3 2.6$\pm $1.3 27.5$\pm $10.6 文献[7] 5.4$\pm $0.8 $-0.3\pm 1.3$ 0.8$\pm $0.1 1.3$\pm $0.3 20.5$\pm $7.4 本文方法 5.3$\pm $0.8 2.1$\pm $1.0 0.7$\pm $0.1 1.2$\pm $0.3 19.0$\pm $6.5 
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表 2 Sliver07数据库分割性能比较(均值$\pm $标准差)
Table 2 Segmentation performance comparison on Sliver07 database (mean $\pm$ std)
方法 VOE (%) RVD (%) ASD (mm) RMSD (mm) MSD (mm) 文献[4] 7.4$\pm $1.9 4.6$\pm $2.8 1.2$\pm $0.4 2.8$\pm $1.3 38.5$\pm $18.0 文献[6] 8.9$\pm $2.2 2.3$\pm $2.0 1.4$\pm $0.3 2.4$\pm $1.2 24.3$\pm $9.6 文献[7] 5.8$\pm $3.2 $-0.1\pm 4.1$ 1.0$\pm $0.5 2.0$\pm $1.2 21.2$\pm $9.3 本文方法 5.7$\pm $3.0 $-0.5\pm 4.0$ 1.1$\pm $0.5 2.1$\pm $1.3 21.5$\pm $10.7
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