DuMeta++: Spatiotemporal Dual Meta-Learning for Generalizable Few-Shot Brain Tissue Segmentation Across Diverse Ages

Accurate segmentation of brain tissues from MRI scans is critical for neuroscience and clinical applications, but achieving consistent performance across the human lifespan remains challenging due to dynamic, age-related changes in brain appearance and morphology. While prior work has sought to mitigate these shifts by using self-supervised regularization with paired longitudinal data, such data are often unavailable in practice. To address this, we propose DuMeta++, a dual meta-learning framework that operates without paired longitudinal data. Our approach integrates: (1) meta-feature learning to extract age-agnostic semantic representations of spatiotemporally evolving brain structures, and (2) meta-initialization learning to enable data-efficient adaptation of the segmentation model. Furthermore, we propose a memory-bank-based class-aware regularization strategy to enforce longitudinal consistency without explicit longitudinal super-vision. We further characterize the convergence of the dual meta-learning pipeline under standard smoothness assumptions. Extensive experiments on the iSeg-2019 and ADNI datasets under one- and five-shot settings demonstrate that DuMeta++ consistently outperforms state-of-the-art methods in both segmentation accuracy and longitudinal consistency. Code will be available at https:// github.com/ladderlab-xjtu/DuMeta++.