Polaffini: 一种基于特征的鲁棒仿射和多项式仿射图像配准方法
Polaffini: A feature-based approach for robust affine and polyaffine image registration
AI总结：
提出Polaffini框架，利用深度学习分割模型提取解剖对应点，通过闭式解实现全局和局部仿射匹配，生成从仿射到多项式仿射的可调平滑变换，在结构对齐和下游非线性配准初始化上优于传统方法。
摘要：
在这项工作中，我们提出了Polaffini，一个稳健且通用的解剖学基础配准框架。医学图像配准主要由基于强度的配准方法主导，这些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明确的解剖对应点进行操作，理论上更理想，但由于可靠提取特征的挑战而 largely 失宠。然而，得益于深度学习的近期进展，这些挑战现已显著克服，预训练的分割模型能够即时提供可靠、精细的解剖描绘。我们旨在证明这些进展可用于创建新的解剖学基础图像配准算法。为此，我们提出Polaffini，它从这些分割区域中以特别简单的方式获得具有一一对应关系的解剖学基础特征点：提取它们的质心。这些特征点通过闭式解实现高效的全局和局部仿射匹配。这些匹配用于生成从仿射到多项式仿射的整体变换，并具有可调平滑度。多项式仿射变换比仿射变换具有更多的自由度，允许更精细的对齐，并且它们在对数-欧几里得框架中的嵌入确保了微分同胚性质。Polaffini既可用于独立配准，也可作为后续非线性配准的预对齐，我们将其与流行的基于强度的配准技术进行了评估。结果表明，Polaffini在结构对齐方面优于竞争方法，并为下游非线性配准提供了改进的初始化。Polaffini快速、稳健且准确，使其特别适合集成到医学图像处理流程中。
英文摘要：
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.
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