Accurate correspondence of complex neuroanatomical surfaces under nonrigid deformations remains a formidable challenge in computational neuroimaging, owing to inter‐subject topological variability, partial occlusions, and non‐isometric distortions. Here, we introduce the Dynamic Graph Analyzer (DGA), a unified hybrid framework that integrates simplified structural descriptors with spatial constraints and formulates matching as a global linear assignment. Structurally, DGA computes node-level metrics, degree weighted by betweenness centrality and local clustering coefficients—to capture essential topological patterns at a low computational cost. Spatially, it employs a two-stage scheme that combines global maximum distances and local rescaling of adjacent node separations to preserve geometric fidelity. By embedding these complementary measures into a single cost matrix solved via the Kuhn-Munkres algorithm and a refinement of weak correspondences, DGA ensures a globally optimal correspondence. In benchmark evaluations on the FAUST dataset, DGA achieved a significant reduction in the mean geodetic reconstruction error compared to models based on graph convolutional learning or traditional spectral methods. Additional experiments demonstrate robust performance on partial matches in TOSCA and cross‐species alignments in SHREC‐20, validating resilience to morphological variation and symmetry ambiguities. These results establish DGA as a scalable and accurate approach for brain shape correspondence, with promising applications in biomarker mapping, developmental studies, and clinical morphometry.
Garcia et al. (Tue,) studied this question.