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Robust Anatomical Landmark Detection with Application to MR Brain Image Registration

机译：鲁棒的解剖地标检测及其在MR脑图像配准中的应用

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摘要

Comparison of human brain MR images is often challenged by large inter-subject structural variability. To determine correspondences between MR brain images, most existing methods typically perform a local neighborhood search, based on certain morphological features. They are limited in two aspects: (1) pre-defined morphological features often have limited power in characterizing brain structures, thus leading to inaccurate correspondence detection, and (2) correspondence matching is often restricted within local small neighborhoods and fails to cater to images with large anatomical difference. To address these limitations, we propose a novel method to detect distinctive landmarks for effective correspondence matching. Specifically, we first annotate a group of landmarks in a large set of training MR brain images. Then, we use regression forest to simultaneously learn (1) the optimal sets of features to best characterize each landmark and (2) the non-linear mappings from the local patch appearances of image points to their 3D displacements towards each landmark. The learned regression forests are used as landmark detectors to predict the locations of these landmarks in new images. Because each detector is learned based on features that best distinguish the landmark from other points and also landmark detection is performed in the entire image domain, our method can address the limitations in conventional methods. The deformation field estimated based on the alignment of these detected landmarks can then be used as initialization for image registration. Experimental results show that our method is capable of providing good initialization even for the images with large deformation difference, thus improving registration accuracy.
机译：人脑MR图像的比较通常受到较大的受试者间结构变异性的挑战。为了确定MR脑图像之间的对应关系，大多数现有方法通常基于某些形态特征来执行局部邻域搜索。它们在两个方面有局限性：（1）预定义的形态特征在表征脑结构方面通常具有有限的功能，从而导致不正确的对应关系检测；（2）对应关系匹配通常限于局部小邻域内且无法满足图像需求具有很大的解剖差异。为了解决这些局限性，我们提出了一种新颖的方法来检测与众不同的界标，以进行有效的对应匹配。具体来说，我们首先在一大组训练MR脑图像中注释一组地标。然后，我们使用回归森林同时学习（1）最佳特征集以最佳地描述每个地标，以及（2）从图像点的局部面片外观到它们向每个地标的3D位移的非线性映射。所学习的回归林用作地标检测器，以预测新图像中这些地标的位置。因为每个检测器都是基于能够最好地区分界标与其他点的特征来学习的，而且界标检测也是在整个图像域中执行的，所以我们的方法可以解决传统方法中的局限性。然后可以将基于这些检测到的界标的对齐方式估计的变形场用作图像配准的初始化。实验结果表明，该方法即使对变形差异较大的图像也能提供良好的初始化效果，从而提高了配准精度。

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