In this work we present Polaffini, a robust and versatile framework for anatomically grounded registration. Medical image registration is dominated by intensity-based registration methods that rely on surrogate measures of alignment quality. In contrast, feature-based approaches that operate by identifying explicit anatomical correspondences, while more desirable in theory, have largely fallen out of favor due to the challenges of reliably extracting features. However, such challenges are now significantly overcome thanks to recent advances in deep learning, which provide pre-trained segmentation models capable of instantly delivering reliable, fine-grained anatomical delineations. We aim to demonstrate that these advances can be leveraged to create new anatomically-grounded image registration algorithms. To this end, we propose Polaffini, which obtains, from these segmented regions, anatomically grounded feature points with 1-to-1 correspondence in a particularly simple way: extracting their centroids. These enable efficient global and local affine matching via closed-form solutions. Those are used to produce an overall transformation ranging from affine to polyaffine with tunable smoothness. Polyaffine transformations can have many more degrees of freedom than affine ones allowing for finer alignment, and their embedding in the log-Euclidean framework ensures diffeomorphic properties. Polaffini has applications both for standalone registration and as pre-alignment for subsequent non-linear registration, and we evaluate it against popular intensity-based registration techniques. Results demonstrate that Polaffini outperforms competing methods in terms of structural alignment and provides improved initialisation for downstream non-linear registration. Polaffini is fast, robust, and accurate, making it particularly well-suited for integration into medical image processing pipelines.

翻译：本文提出Polaffini，一个鲁棒且通用的解剖学基础配准框架。医学图像配准领域目前主要由基于强度的配准方法主导，这类方法依赖于对齐质量的替代度量。相比之下，基于特征的方法通过识别显式解剖对应点进行操作，虽然在理论上更理想，但由于可靠提取特征的挑战，已基本不再受青睐。然而，得益于深度学习的最新进展，这些挑战现已得到显著克服——预训练的分割模型能够即时提供可靠、细粒度的解剖轮廓。我们旨在证明，可以利用这些进展创建新型的解剖学基础图像配准算法。为此，我们提出Polaffini方法，该方法以特别简单的方式从这些分割区域中获取具有一一对应关系的解剖学基础特征点：提取其质心。这些特征点通过闭式解实现了高效的全局与局部仿射匹配。基于此，可生成从仿射到多仿射、平滑度可调的整体变换。多仿射变换比仿射变换具有更多自由度，可实现更精细的对齐，且其在对数欧几里得框架中的嵌入保证了微分同胚特性。Polaffini既可用于独立配准，也可作为后续非线性配准的预对齐步骤，我们将其与主流基于强度的配准技术进行了对比评估。结果表明，Polaffini在结构对齐方面优于现有方法，并为下游非线性配准提供了更优的初始化。Polaffini具有快速、鲁棒和准确的特点，使其特别适合集成到医学图像处理流程中。