从灌注MR图像中建立三维概率肝图谱的平均集

E. Durá, J. Domingo, A. F. Rojas-Arboleda, L. Martí-Bonmatí
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引用次数: 3

摘要

本文是关于肝图谱的构建。在计算腹部解剖学框架中最重要的问题之一是定义一个图谱,为常见的医学任务(如配准和分割)提供先验信息。与迄今为止(据我们所知)已经提出的其他方法不同,在本文中,我们建议使用随机紧凑平均集的概念来构建概率肝脏地图集。为了完成这项任务,采用了两层工艺。首先,一组3D图像由医生手工分割。我们把不同的三维分割形状看作是一个随机紧集的实现。其次,应用两种已知的平均集定义的元素来构建一个概率图谱,该图谱捕获了病例的可变性,同时保持了肝脏的基本形状。
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Mean sets for building 3D probabilistic liver atlas from perfusion MR images
This paper is concerned with liver atlas construction. One of the most important issues in the framework of computational abdominal anatomy is to define an atlas that provides a priori information for common medical task such as registration and segmentation. Unlike other approaches already proposed so far (to our knowledge), in this paper we propose to use the concept of random compact mean set to build probabilistic liver atlases. To accomplish this task a two-tier process was carried out. First a set of 3D images was manually segmented by a physician. We see the different 3D segmented shapes as a realization of a random compact set. Secondly, elements of two known definitions of mean set were applied to build a probabilistic atlas that captures the variability of the cases, keeping nevertheless the essential shape of the liver.
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