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首页> 外文期刊>Academic radiology >Automated segmentation of the liver from 3D CT images using probabilistic atlas and multilevel statistical shape model.
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Automated segmentation of the liver from 3D CT images using probabilistic atlas and multilevel statistical shape model.

机译：使用概率图谱和多级统计形状模型从3D CT图像自动分割肝脏。

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RATIONALE AND OBJECTIVES: An atlas-based automated liver segmentation method from three-dimensional computed tomographic (3D CT) images has been developed. The method uses two types of atlases, a probabilistic atlas (PA) and a statistical shape model (SSM). MATERIALS AND METHODS: Voxel-based segmentation with a PA is first performed to obtain a liver region, then the obtained region is used as the initial region for subsequent SSM fitting to 3D CT images. To improve reconstruction accuracy, particularly for highly deformed livers, we use a multilevel SSM (ML-SSM). In ML-SSM, the entire shape is divided into patches, with principal component analysis applied to each patch. To avoid inconsistency among patches, we introduce a new constraint called the "adhesiveness constraint" for overlapping regions among patches. RESULTS: The PA and ML-SSM were constructed from 20 training datasets. We applied the proposed method to eight evaluation datasets. On average, volumetric overlap of 89.2 +/- 1.4% and average distance of 1.36 +/- 0.19 mm were obtained. CONCLUSIONS: The proposed method was shown to improve segmentation accuracy for datasets including highly deformed livers. We demonstrated that segmentation accuracy is improved using the initial region obtained with PA and the introduced constraint for ML-SSM.
机译：理由和目的：已经开发了一种基于图集的三维肝脏断层扫描（3D CT）图像自动肝分割方法。该方法使用两种类型的图集，即概率图集（PA）和统计形状模型（SSM）。材料与方法：首先用PA进行基于体素的分割，以获得肝脏区域，然后将所获得的区域用作初始区域，以用于随后的SSM拟合3D CT图像。为了提高重建精度，特别是对于高度变形的肝脏，我们使用了多层SSM（ML-SSM）。在ML-SSM中，将整个形状分为多个小块，并对每个小块应用主成分分析。为了避免补丁之间的不一致，我们针对补丁之间的重叠区域引入了一个称为“粘附性约束”的新约束。结果：PA和ML-SSM是从20个训练数据集中构建的。我们将提出的方法应用于八个评估数据集。平均而言，获得了89.2 +/- 1.4％的体积重叠和1.36 +/- 0.19mm的平均距离。结论：所提出的方法被证明可以提高包括高度变形肝脏在内的数据集的分割精度。我们证明了使用PA获得的初始区域和引入的ML-SSM约束可以提高分割精度。

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