  Brain tissue segmentation is essential for neuroscience and clinical studies.
However, segmentation on longitudinal data is challenging due to dynamic brain
changes across the lifespan. Previous researches mainly focus on
self-supervision with regularizations and will lose longitudinal generalization
when fine-tuning on a specific age group. In this paper, we propose a dual
meta-learning paradigm to learn longitudinally consistent representations and
persist when fine-tuning. Specifically, we learn a plug-and-play feature
extractor to extract longitudinal-consistent anatomical representations by
meta-feature learning and a well-initialized task head for fine-tuning by
meta-initialization learning. Besides, two class-aware regularizations are
proposed to encourage longitudinal consistency. Experimental results on the
iSeg2019 and ADNI datasets demonstrate the effectiveness of our method. Our
code is available at https://github.com/ladderlab-xjtu/DuMeta.
