Segmentation of multiple organs in non-contrast 3D abdominal CT images
Segmentation of multiple organs in non-contrast 3D abdominal CT images
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DOI：
10.1007/s11548-007-0135-z
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发表时间：
2007-12-01
影响因子：
3
通讯作者：
Smutek, Daniel
中科院分区：
文献类型：
--
作者：
Shimizu, Akinobu;Ohno, Rena;Smutek, Daniel
目的提出一种从非对比度三维腹部CT图像中同时提取12个器官的方法。材料与方法该方法采用腹腔标准化过程和图谱引导分割，结合EM算法的参数估计，以处理受试者之间特征分布参数的大幅波动。然后使用多个水平集进行分割，该水平集最小化能量函数，该能量函数考虑器官之间的层次性和排他性以及器官中灰度值的均匀性。为了评估所提出的方法的性能，10个非对比度的三维CT volumes.Results的特征分布参数估计的准确性略有提高，使用建议的EM方法，从而在更好的分割过程中的性能。与没有提出的参数估计过程的结果相比，十二个器官中有九个器官在统计学上得到了改善。与图谱引导分割相比，所提出的多水平集还将分割的性能平均提高了7.2个点。12个器官中有9个器官的分割效果优于图谱引导方法。结论该方法在三维CT体积分割中具有更好的分割效果。
Objective We propose a simultaneous extraction method for 12 organs from non-contrast three-dimensional abdominal CT images.Materials and methods The proposed method uses an abdominal cavity standardization process and atlas guided segmentation incorporating parameter estimation with the EM algorithm to deal with the large fluctuations in the feature distribution parameters between subjects. Segmentation is then performed using multiple level sets, which minimize the energy function that considers the hierarchy and exclusiveness between organs as well as uniformity of grey values in organs. To assess the performance of the proposed method, ten non-contrast 3D CT volumes were used.Results The accuracy of the feature distribution parameter estimation was slightly improved using the proposed EM method, resulting in better performance of the segmentation process. Nine organs out of twelve were statistically improved compared with the results without the proposed parameter estimation process. The proposed multiple level sets also boosted the performance of the segmentation by 7.2 points on average compared with the atlas guided segmentation. Nine out of twelve organs were confirmed to be statistically improved compared with the atlas guided method.Conclusion The proposed method was statistically proved to have better performance in the segmentation of 3D CT volumes.