Dual Meta-Learning with Longitudinally Generalized Regularization for One-Shot Brain Tissue Segmentation Across the Human Lifespan

• URL: http://arxiv.org/abs/2308.06774v1
• Date: Sun, 13 Aug 2023 14:02:27 GMT
• Title: Dual Meta-Learning with Longitudinally Generalized Regularization for One-Shot Brain Tissue Segmentation Across the Human Lifespan
• Authors: Yongheng Sun, Fan Wang, Jun Shu, Haifeng Wang, Li Wang. Deyu Meng, Chunfeng Lian
• Abstract summary: 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. Experimental results on the iSeg 2019 and ADNI datasets demonstrate the effectiveness of our method.
• Score: 30.57799149219082
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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