Automated liver segmentation from a postmortem CT scan based on a statistical shape model
Automated liver segmentation from a postmortem CT scan based on a statistical shape model
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DOI：
10.1007/s11548-016-1481-5
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发表时间：
2017-02
影响因子：
3
通讯作者：
Atsushi Saito;Seiji Yamamoto;S. Nawano;A. Shimizu
中科院分区：
文献类型：
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作者：
Atsushi Saito;Seiji Yamamoto;S. Nawano;A. Shimizu
目的由于严重的病理和/或死后变化引起的大变形和强度变化，从死后计算机断层扫描 (PMCT) 体积中进行自动肝脏分割是一个具有挑战性的问题。本文通过一种新颖的分割算法解决了这个问题，该算法使用死后肝脏的统计形状模型（SSM）。方法通过所提出的 SSM 引导的期望最大化（EM）算法直接从给定体积估计肝脏的位置和形状参数，而无需任何可能因大变形和强度变化而失败的空间标准化。然后将估计的位置和形状参数用作后续基于图切割的精细分割过程的约束。使用 144 个活体肝脏和 32 个死后肝脏训练具有八种不同 SSM 的算法，并以双重交叉验证方式在 32 个死后肝脏上测试分割算法。分割性能通过分割结果和真实肝脏标签之间的杰卡德指数（JI）来衡量。结果最佳SSM的分割结果的平均JI为0.8501，与使用传统SSM获得的结果和之前的死后肝脏分割的结果相比更好，具有统计显着性差异。结论我们提出了一种从PMCT体积自动肝脏分割的算法，其中SSM引导的EM算法估计给定肝脏的位置和形状参数音量准确。我们使用实际的尸检 CT 体积证明了所提出算法的有效性。
PurposeAutomated liver segmentation from a postmortem computed tomography (PMCT) volume is a challenging problem owing to the large deformation and intensity changes caused by severe pathology and/or postmortem changes. This paper addresses this problem by a novel segmentation algorithm using a statistical shape model (SSM) for a postmortem liver.MethodsThe location and shape parameters of a liver are directly estimated from a given volume by the proposed SSM-guided expectation–maximization (EM) algorithm without any spatial standardization that might fail owing to the large deformation and intensity changes. The estimated location and shape parameters are then used as a constraint of the subsequent fine segmentation process based on graph cuts. Algorithms with eight different SSMs were trained using 144 in vivo and 32 postmortem livers, and the segmentation algorithm was tested on 32 postmortem livers in a twofold cross validation manner. The segmentation performance is measured by the Jaccard index (JI) between the segmentation result and the true liver label.ResultsThe average JI of the segmentation result with the best SSM was 0.8501, which was better compared with the results obtained using conventional SSMs and the results of the previous postmortem liver segmentation with statistically significant difference.ConclusionsWe proposed an algorithm for automated liver segmentation from a PMCT volume, in which an SSM-guided EM algorithm estimated the location and shape parameters of a liver in a given volume accurately. We demonstrated the effectiveness of the proposed algorithm using actual postmortem CT volumes.