Probabilistic atlas and geometric variability estimation to drive tissue segmentation
Probabilistic atlas and geometric variability estimation to drive tissue segmentation
复制标题
概率图集和几何变异性估计驱动组织分割
DOI：
10.1002/sim.6156
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发表时间：
2014
影响因子：
2
通讯作者：
S. Allassonnière
中科院分区：
文献类型：
--
作者：
Hao Xu;B. Thirion;S. Allassonnière
计算机解剖图谱在医学图像分析中起着重要的作用。虽然地图集通常指的是标准或平均图像，也称为模板，它可能很好地代表了给定的种群，但它不足以描述观察到的种群的详细特征。模板图像应该与观测中表示的形状的几何可变性一起学习。这两个数量将在后面形成相应人口的地图集。几何变异性被建模为模板图像的变形，使其与观测值拟合。在本文中，我们提供了一个基于密集可变形模板的新生成统计模型的详细分析，该模型代表了医学图像中观察到的几种组织类型。我们的图谱包含每个组织（称为类）的概率图的估计和变形度量。我们使用随机算法来估计给定数据集的概率图谱。然后将该图谱用于基于图谱的分割方法来分割新图像。实验显示在大脑T1 MRI数据集上。版权所有©2014 John Wiley & Sons， Ltd。
Computerized anatomical atlases play an important role in medical image analysis. While an atlas usually refers to a standard or mean image also called template, which presumably represents well a given population, it is not enough to characterize the observed population in detail. A template image should be learned jointly with the geometric variability of the shapes represented in the observations. These two quantities will in the sequel form the atlas of the corresponding population. The geometric variability is modeled as deformations of the template image so that it fits the observations. In this paper, we provide a detailed analysis of a new generative statistical model based on dense deformable templates that represents several tissue types observed in medical images. Our atlas contains both an estimation of probability maps of each tissue (called class) and the deformation metric. We use a stochastic algorithm for the estimation of the probabilistic atlas given a dataset. This atlas is then used for atlas‐based segmentation method to segment the new images. Experiments are shown on brain T1 MRI datasets. Copyright © 2014 John Wiley & Sons, Ltd.