Active Shape Models for a Fully Automated 3D Segmentation of the Liver - An Evaluation on Clinical Data
Active Shape Models for a Fully Automated 3D Segmentation of the Liver - An Evaluation on Clinical Data
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DOI：
10.1007/11866763_6
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发表时间：
2006-10
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影响因子：
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通讯作者：
T. Heimann;I. Wolf;H. Meinzer
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文献类型：
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作者：
T. Heimann;I. Wolf;H. Meinzer
本文评估了三维活动形状模型 (ASM) 在 48 次临床 CT 扫描中分割肝脏的性能。所采用的形状模型是使用基于最小描述长度 (MDL) 的优化方法从 32 个样本构建的。创建三种不同的灰度值外观模型（简单强度、梯度和归一化梯度剖面）来指导搜索。所采用的分割技术是具有 10 种和 30 种变化模式的 ASM 搜索以及与具有 10 种变化模式的形状模型耦合的可变形模型。为了评估分割性能，将获得的结果与采用四种不同度量（重叠、平均距离、RMS 距离和大于 5mm 的偏差比率）的手动分割进行比较。唯一提供可用结果的外观模型是归一化梯度剖面。可变形模型搜索取得了最好的结果，其次是具有 30 种模式的 ASM 搜索。总体而言，统计形状建模为肝脏的全自动分割提供了非常有希望的结果。
This paper presents an evaluation of the performance of a three-dimensional Active Shape Model (ASM) to segment the liver in 48 clinical CT scans. The employed shape model is built from 32 samples using an optimization approach based on the minimum description length (MDL). Three different gray-value appearance models (plain intensity, gradient and normalized gradient profiles) are created to guide the search. The employed segmentation techniques are ASM search with 10 and 30 modes of variation and a deformable model coupled to a shape model with 10 modes of variation. To assess the segmentation performance, the obtained results are compared to manual segmentations with four different measures (overlap, average distance, RMS distance and ratio of deviations larger 5mm). The only appearance model delivering usable results is the normalized gradient profile. The deformable model search achieves the best results, followed by the ASM search with 30 modes. Overall, statistical shape modeling delivers very promising results for a fully automated segmentation of the liver.