Mandibular canal segmentation using 3D Active Appearance Models and shape context registration

F. Abdolali, R. Zoroofi
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引用次数: 7

Abstract

This paper presents a method for automatic segmentation of mandibular canal from CBCT (cone beam CT) images based on 3D Active Appearance Models (AAM) and shape context registration. The proposed algorithm consists of two stages: Firstly, Shape Context based non-rigid surface registration of the manual segmented images is used to obtain the point correspondence between the given training cases. Subsequently, an AAM is used to segment the mandibular canal on 60 training cases. The method is evaluated using a 5-fold cross validation over 5 repetitions. The mean Dice similarity coefficient and 95% Hausdorff distance are 0.86 and 0.90 mm, respectively.
下颌管分割使用三维主动外观模型和形状上下文配准
提出了一种基于三维活动外观模型(AAM)和形状上下文配准的锥形束CT (CBCT)下颌管图像自动分割方法。该算法分为两个阶段:首先,对人工分割图像进行基于形状上下文的非刚性表面配准,得到给定训练案例之间的点对应关系;随后,用AAM对60例训练病例进行下颌管分割。该方法使用5次重复的5倍交叉验证进行评估。Dice相似系数均值为0.86 mm, 95% Hausdorff距离均值为0.90 mm。
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