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Feature‐based groupwise registration by hierarchical anatomical correspondence detection

机译：通过分层解剖对应检测实现基于特征的分组配准

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摘要

Groupwise registration has been widely investigated in recent years due to its importance in analyzing population data in many clinical applications. To our best knowledge, most of the groupwise registration algorithms only utilize the intensity information. However, it is well known that using intensity only is not sufficient to achieve the anatomically sound correspondences in medical image registration. In this article, we propose a novel feature‐based groupwise registration algorithm to establish the anatomical correspondence across subjects by using the attribute vector that is defined as the morphological signature for each voxel. Similar to most of the state‐of‐the‐art groupwise registration algorithms, which simultaneously estimate the transformation fields for all subjects, we develop an energy function to minimize the intersubject discrepancies on anatomical structures and drive all subjects toward the hidden common space. To make the algorithm efficient and robust, we decouple the complex groupwise registration problem into two easy‐to‐solve subproblems, namely (1) robust correspondence detection and (2) dense transformation field estimation, which are systematically integrated into a unified framework. To achieve the robust correspondences in the step (1), several strategies are adopted. First, the procedure of feature matching is evaluated within a neighborhood, rather than only on a single voxel. Second, the driving voxels with distinctive image features are designed to drive the transformations of other nondriving voxels. Third, we take advantage of soft correspondence assignment not only in the spatial domain but also across the population of subjects. Specifically, multiple correspondences are allowed to alleviate the ambiguity in establishing correspondences w.r.t. a particular subject and also the contributions from different subjects are dynamically controlled throughout the registration. Eventually in the step (2), based on the correspondences established for the driving voxels, thin‐plate spline is used to propagate correspondences on the driving voxels to other locations in the image. By iteratively repeating correspondence detection and dense deformation estimation, all the subjects will be aligned onto the common space. Our feature‐based groupwise registration algorithm has been extensively evaluated over 18 elderly brains, 16 brains from NIREP (with 32 manually delineated labels), 40 brains from LONI LPBA40 (with 54 manually delineated labels), and 12 pairs of normal controls and simulated atrophic brain images. In all experiments, our algorithm achieves more robust and accurate registration results, compared with another groupwise algorithm and a pairwise registration method. Hum Brain Mapp, 2012. © 2011 Wiley Periodicals, Inc.
机译：近年来，基于分组的注册在分析许多临床应用中的人群数据方面具有重要意义，因此已被广泛研究。据我们所知，大多数分组配准算法仅利用强度信息。然而，众所周知，仅使用强度不足以在医学图像配准中获得解剖学上的声音对应。在本文中，我们提出了一种新颖的基于特征的逐组配准算法，该算法通过使用定义为每个体素的形态学特征的属性矢量来建立对象之间的解剖学对应关系。与大多数最新的成组配准算法相似，它们可以同时估计所有对象的变换场，我们开发了一个能量函数，以最大程度地减少对象之间在解剖结构上的差异，并将所有对象驱向隐藏的公共空间。为了使算法高效且健壮，我们将复杂的分组配准问题分解为两个易于解决的子问题，即（1）鲁棒的对应检测和（2）密集的变换域估计，它们被系统地集成到一个统一的框架中。为了在步骤（1）中获得鲁棒的对应关系，采用了几种策略。首先，特征匹配的过程是在附近而不是仅在单个体素上进行评估的。其次，具有独特图像特征的驾驶体素旨在驱动其他非驾驶体素的转换。第三，我们不仅在空间领域中而且在整个主题人群中都利用了软通信分配。具体地，允许多个对应关系减轻建立对应关系的歧义。在整个注册过程中，将动态控制特定主题以及来自不同主题的贡献。最终，在步骤（2）中，基于为驱动体素建立的对应关系，使用薄板样条将驱动体素上的对应关系传播到图像中的其他位置。通过反复重复进行对应检测和密集变形估计，所有对象都将对准公共空间。我们的基于特征的分组配准算法已针对18个老年大脑，来自NIREP的16个大脑（带有32个手动描绘的标签），来自LONI LPBA40的40个大脑（带有54个手动描绘的标签）以及12对正常对照和模拟的萎缩性大脑进行了广泛评估。脑图像。在所有实验中，与另一个分组算法和成对注册方法相比，我们的算法可实现更鲁棒和准确的注册结果。嗡嗡声大脑Mapp，2012年。©2011 Wiley Periodicals，Inc.

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