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

• URL: http://arxiv.org/abs/2602.07174v1
• Date: Fri, 06 Feb 2026 20:21:19 GMT
• Title: DuMeta++: Spatiotemporal Dual Meta-Learning for Generalizable Few-Shot Brain Tissue Segmentation Across Diverse Ages
• Authors: Yongheng Sun, Jun Shu, Jianhua Ma, Fan Wang,
• Abstract summary: We propose emphDuMeta++, a dual meta-learning framework that operates without paired longitudinal data.<n>Our approach integrates: (1) meta-feature learning to extract age-agnostic semantic representations of brain structures, and (2) meta-initialization learning to enable data-efficient adaptation of the segmentation model.
• Score: 14.361008329377498
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 \emph{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 supervision. We theoretically prove the convergence of our DuMeta++, ensuring stability. Experiments on diverse datasets (iSeg-2019, IBIS, OASIS, ADNI) under few-shot settings demonstrate that DuMeta++ outperforms existing methods in cross-age generalization. Code will be available at https://github.com/ladderlab-xjtu/DuMeta++.

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