A Stochastic Large Deformation Model for Computational Anatomy
A Stochastic Large Deformation Model for Computational Anatomy
复制标题
计算解剖学的随机大变形模型
DOI：
10.1007/978-3-319-59050-9_45
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
S. Sommer
中科院分区：
文献类型：
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作者：
Alexis Arnaudon;Darryl D. Holm;A. Pai;S. Sommer
在使用计算解剖学对人体器官形状的研究中，发现变异是由主体间解剖差异、疾病特异性效应和测量噪声引起的。本文介绍了一种将随机变化纳入大变形微分同构度量映射（LDDMM）框架的随机模型。考虑到LDDMM在特定场景下的随机性，提出了带噪声地标的模板估计问题，并给出了两种从指定数据集中有效估计噪声场参数的方法。一种方法是通过一组有限的微分方程直接逼近每个地标方差的时间演化，另一种方法是基于期望最大化算法。在第二种方法中，通过使用大变形梯度流算法的随机摄动版本进行桥梁采样，在不注册地标的情况下实现了数据似然性的评估。在人体胼胝体的合成样例和形状数据上对该方法和算法进行了实验验证。
In the study of shapes of human organs using computational anatomy, variations are found to arise from inter-subject anatomical differences, disease-specific effects, and measurement noise. This paper introduces a stochastic model for incorporating random variations into the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework. By accounting for randomness in a particular setup which is crafted to fit the geometrical properties of LDDMM, we formulate the template estimation problem for landmarks with noise and give two methods for efficiently estimating the parameters of the noise fields from a prescribed data set. One method directly approximates the time evolution of the variance of each landmark by a finite set of differential equations, and the other is based on an Expectation-Maximisation algorithm. In the second method, the evaluation of the data likelihood is achieved without registering the landmarks, by applying bridge sampling using a stochastically perturbed version of the large deformation gradient flow algorithm. The method and the estimation algorithms are experimentally validated on synthetic examples and shape data of human corpora callosa.