Automated Segmentation of the Liver from 3D CT Images Using Probabilistic Atlas and Multi-level Statistical Shape Model
Automated Segmentation of the Liver from 3D CT Images Using Probabilistic Atlas and Multi-level Statistical Shape Model
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DOI：
10.1007/978-3-540-75757-3_11
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发表时间：
2007-10
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影响因子：
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通讯作者：
T. Okada;Ryuji Shimada;Yoshinobu Sato;M. Hori;Keita Yokota;M. Nakamoto;Yenwei Chen;Hironobu Nakamura;S. Tamura
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文献类型：
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作者：
T. Okada;Ryuji Shimada;Yoshinobu Sato;M. Hori;Keita Yokota;M. Nakamoto;Yenwei Chen;Hironobu Nakamura;S. Tamura
描述了一种基于地图集的三维CT图像自动肝脏分割方法。该方法利用两种类型的地图集，即概率地图集（PA）和统计形状模型（SSM）。首先使用PA进行基于体素的分割，得到肝脏区域，然后将得到的区域作为初始区域，用于后续的SSM拟合到3D CT图像。为了提高重建的准确性，特别是对于严重变形的肝脏，我们使用多层次SSM （ML-SSM）。在ML-SSM中，将整个形状划分为小块，并对每个小块进行主成分分析。为了避免贴片之间的不一致性，我们引入了一种新的贴片重叠区域的粘附性约束。在实验中，我们证明了使用PA获得的初始区域和ML-SSM引入的约束可以提高分割精度。
An atlas-based automated liver segmentation method from 3D CT images is described. The method utilizes two types of atlases, that is, the probabilistic atlas (PA) and statistical shape model (SSM). Voxel-based segmentation with PA is firstly performed to obtain a liver region, and then the obtained region is used as the initial region for subsequent SSM fitting to 3D CT images. To improve reconstruction accuracy especially for largely deformed livers, we utilize a multi-level SSM (ML-SSM). In ML-SSM, the whole shape is divided into patches, and principal component analysis is applied to each patches. To avoid the inconsistency among patches, we introduce a new constraint called the adhesiveness constraint for overlap regions among patches. In experiments, we demonstrate that segmentation accuracy improved by using the initial region obtained with PA and the introduced constraint for ML-SSM.