Bayesian inference for uncertainty quantification in point-based deformable image registration
Bayesian inference for uncertainty quantification in point-based deformable image registration
复制标题
基于点的变形图像配准中不确定性量化的贝叶斯推理
DOI：
10.1117/12.2512988
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
J. Ehrhardt
中科院分区：
文献类型：
--
作者：
S. Schultz;J. Krüger;H. Handels;J. Ehrhardt
在图像引导诊断中，通常根据配准的图像数据来决定患者的治疗。在配准过程中可能会出现错误，例如，由于不正确的模型假设或由于图像伪影或病理导致的不对应区域。因此，分析配准结果的准确性和可靠性的方法研究近年来变得越来越重要。一种量化配准不确定性的方法是基于变换参数的后验分布。由于后验分布的精确计算是难以处理的，变分贝叶斯推理可以有效地提供一个近似解。最近，一种基于强度的配准的概率方法已经被开发出来，它使用稀疏的基于点的图像表示，并显示出处理损坏数据的内在能力。自然输出是两个点集之间的对应概率，它为潜在的不对应和因此不正确变形的区域提供了度量。为了对配准不确定性和对应概率进行比较分析，我们在变分贝叶斯框架中集成了一种基于点的非线性概率配准方法。所开发的方法应用于脑病变的MR图像，其中两种测量显示适度的相关性，但相对于改变的正则化有不同的行为。此外，我们模拟真实的地面真实数据，以允许测量和局部配准误差之间的相关性分析。实际上，由于模型差异导致的配准误差不能用配准不确定性来描述，但是，在存在损坏图像区域的情况下，可以发现很强的相关性。
In image guided diagnostics the treatment of patients is often decided based on registered image data. During the registration process errors can occur, e.g., due to incorrect model assumptions or non-corresponding areas due to image artifacts or pathologies. Therefore, the study of approaches that analyze the accuracy and reliability of registration results has become increasingly important in recent years. One way to quantify registration uncertainty is based on the posterior distribution of the transformation parameters. Since the exact computation of the posterior distribution is intractable, variational Bayes inference can be used to efficiently provide an approximate solution. Recently, a probabilistic approach to intensity-based registration has been developed that uses sparse point-based representations of images and shows an intrinsic ability to deal with corrupted data. A natural output are correspondence probabilities between the two point sets which provide a measure for potentially non-corresponding and thus incorrectly deformed regions. In order to perform a comparative analysis of registration uncertainty and correspondence probabilities, we integrate a nonlinear point-based probabilistic registration method in a variational Bayesian framework. The developed method is applied to MR images with brain lesions, where both measures show moderate correlations, but a different behavior with respect to altered regularization. Further, we simulate realistic ground-truth data to allow for a correlation analysis between both measures and local registration errors. In fact, registration errors due to model differences cannot be depicted by registration uncertainty, however, in the presence of corrupted image areas, a strong correlation can be found.
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