Surface-based cortical registration is an important topic in medical image analysis and facilitates many downstream applications. Current approaches for cortical registration are mainly driven by geometric features, such as sulcal depth and curvature, and often assume that registration of folding patterns leads to alignment of brain function. However, functional variability of anatomically corresponding areas across subjects has been widely reported, particularly in higher-order cognitive areas. In this work, we present JOSA, a novel cortical registration framework that jointly models the mismatch between geometry and function while simultaneously learning an unbiased population-specific atlas. Using a semi-supervised training strategy, JOSA achieves superior registration performance in both geometry and function without requiring functional data at inference. This learning framework can be extended to any auxiliary data to guide spherical registration that is available during training but is difficult or impossible to obtain during inference, such as parcellations, architectonic identity, transcriptomic information, and molecular profiles.

翻译：基于表面的皮质配准是医学图像分析中的重要课题，其能促进许多下游应用。当前皮质配准方法主要依赖几何特征（如脑沟深度和曲率），且通常假设折叠模式的配准可导致脑功能对齐。然而，已有大量研究报道个体间解剖对应区域的功能变异性，尤其是高级认知区域。本研究提出JOSA——一种新型皮质配准框架，该框架在联合建模几何与功能错配的同时，同步学习无偏群体特异性图谱。通过半监督训练策略，JOSA在无需推理时功能数据的情况下，实现了几何与功能两方面的优越配准性能。本学习框架可扩展至任何在训练阶段可用但推理阶段难以或无法获取的辅助数据（如分区图、构筑学标识、转录组信息及分子图谱）来指导球面配准。