Abstract
In human neuroimaging studies, atlas registration enables mapping MRI scans to a common coordinate frame, which is necessary to aggregate data from multiple subjects. Machine learning registration methods have achieved excellent speed and accuracy but lack interpretability. More recently, keypoint-based methods have been proposed to tackle this issue, but their accuracy is still subpar, particularly when fitting nonlinear transforms. Here we propose Registration by Regression (RbR), a novel atlas registration framework that is highly robust and flexible, conceptually simple, and can be trained with cheaply obtained data. RbR predicts the (x,y,z) atlas coordinates for every voxel of the input scan (i.e., every voxel is a keypoint), and then uses closed-form expressions to quickly fit transforms using a wide array of possible deformation models, including affine and nonlinear (e.g., Bspline, Demons, invertible diffeomorphic models, etc.). Robustness is provided by the large number of voxels informing the registration and can be further increased by robust estimators like RANSAC. Experiments on independent public datasets show that RbR yields more accurate registration than competing keypoint approaches, while providing full control of the deformation model.
Abstract (translated)
在人类神经影像研究中，空间映射允许将MRI扫描映射到共同的坐标框架中，这对于从多个受试者中汇总数据是必要的。机器学习配准方法取得了良好的速度和精度，但缺乏可解释性。最近，基于关键点的配准方法提出了来解决这个 issue，但它们的准确性仍然较低，特别是在拟合非线性变换时。因此，我们提出了一个名为注册 by 回归 (RbR) 的新的配准框架，它具有很高的稳健性和灵活性，从低廉的数据中进行训练，并且具有直观简单的概念。RbR预测输入扫描中每个体素（即每个体素是一个关键点）的 (x,y,z) 配准坐标，然后使用一系列可能的变化模型（包括线性变换、非线性变换等）来使用闭式公式快速拟合变换。通过大量的体素的信息进行注册，可以进一步增加稳健性，而RANSAC等 robust estimator 可以使这种效果得到改善。在独立公共数据集上的实验表明，RbR 产生的配准比竞争性的关键点方法更准确，同时提供了对变形模型的完全控制。
URL
https://arxiv.org/abs/2404.16781
