一种高效的模型驱动群组配准方法用于图谱构建
An Efficient Model-Driven Groupwise Approach for Atlas Construction
AI总结：
本文提出模型驱动的群组配准框架DARC，通过坐标下降策略和中心性激活函数高效构建无偏微分同胚图谱，并在一次性分割和形状合成任务中取得优异表现。
摘要：
图谱构建是医学图像分析的基础，为群体级解剖建模等任务提供标准化的空间参考。虽然数据驱动的配准方法最近在成对配准场景中展现出潜力，但它们对大型训练数据集的依赖、有限的泛化能力以及在群组场景中缺乏真正的推理阶段，阻碍了其实际应用。相比之下，模型驱动方法提供了无需训练、具有理论依据且数据高效的替代方案，但在应用于大型3D数据集时往往面临可扩展性和优化方面的挑战。在本文中，我们提出了DARC（Diffeomorphic Atlas Registration via Coordinate descent，基于坐标下降的微分同胚图谱配准），这是一种新颖的模型驱动群组配准框架，用于图谱构建。DARC支持广泛的图像不相似性度量，并能高效处理任意数量的3D图像，而不会产生GPU内存问题。通过坐标下降策略和中心性强制的激活函数，DARC能够生成无偏、微分同胚且具有高解剖保真度的图谱。除图谱构建外，我们展示了两个关键应用：（1）一次性分割，仅在图谱上标注的标签通过逆形变传播到各个受试者，其性能优于最先进的少样本方法；（2）形状合成，通过使用合成的微分同胚形变场对图谱网格进行形变来生成新的解剖变体。总体而言，DARC为图谱构建及其应用提供了一个灵活、可泛化且资源高效的框架。
英文摘要：
Atlas construction is fundamental to medical image analysis, offering a standardized spatial reference for tasks such as population-level anatomical modeling. While data-driven registration methods have recently shown promise in pairwise settings, their reliance on large training datasets, limited generalizability, and lack of true inference phases in groupwise contexts hinder their practical use. In contrast, model-driven methods offer training-free, theoretically grounded, and data-efficient alternatives, though they often face scalability and optimization challenges when applied to large 3D datasets. In this work, we introduce DARC (Diffeomorphic Atlas Registration via Coordinate descent), a novel model-driven groupwise registration framework for atlas construction. DARC supports a broad range of image dissimilarity metrics and efficiently handles arbitrary numbers of 3D images without incurring GPU memory issues. Through a coordinate descent strategy and a centrality-enforcing activation function, DARC produces unbiased, diffeomorphic atlases with high anatomical fidelity. Beyond atlas construction, we demonstrate two key applications: (1) One-shot segmentation, where labels annotated only on the atlas are propagated to subjects via inverse deformations, outperforming state-of-the-art few-shot methods; and (2) shape synthesis, where new anatomical variants are generated by warping the atlas mesh using synthesized diffeomorphic deformation fields. Overall, DARC offers a flexible, generalizable, and resource-efficient framework for atlas construction and applications.
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