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Automated anatomical labeling of abdominal arteries and hepatic portal system extracted from abdominal CT volumes

机译:从腹部CT量提取的腹部动脉和肝门系统的自动解剖标记

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This paper proposes a method for automated anatomical labeling of abdominal arteries and a hepatic portal system. In abdominal surgeries, understanding blood vessel structure is critical since it is very complicated. The input of the proposed method is the blood vessel region extracted from the CT volume. The blood vessel region is expressed as a tree structure by applying a thinning process to it and compute the mapping from the branches in the tree structure to the anatomical names. First, several characteristic anatomical names are assigned by rule-based pre-processing. The branches assigned to these names are used as references. The remaining blood vessel names are assigned using a likelihood function trained by a machine-learning technique. Simple rule-based postprocessing can correct several blood vessel names. The output of the proposed method is a tree structure with anatomical names. In an experiment using 50 blood vessel regions manually extracted from abdominal CT volumes, the recall and precision rates of the abdominal arteries were 86.2% and 85.3%, and they were 86.5% and 79.5% for the hepatic portal system. (C) 2014 Elsevier B.V. All rights reserved.
机译:本文提出了一种自动解剖标记腹部动脉和肝门系统的方法。在腹部手术中,了解血管结构至关重要,因为它非常复杂。所提出的方法的输入是从CT量中提取的血管区域。通过对其进行细化处理并计算从树结构中的分支到解剖名称的映射,可以将血管区域表示为树结构。首先,通过基于规则的预处理分配几个特征解剖名称。分配给这些名称的分支用作参考。使用通过机器学习技术训练的似然函数来分配其余的血管名称。简单的基于规则的后处理可以纠正多个血管名称。所提出的方法的输出是具有解剖名称的树结构。在使用从腹部CT体积中手动提取的50个血管区域的实验中,腹部动脉的召回率和准确率分别为86.2%和85.3%,对于肝门系统,它们的分别为86.5%和79.5%。 (C)2014 Elsevier B.V.保留所有权利。

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