首页|期刊导航|Magnetic Resonance Letters|Self-supervised pre-training based hybrid network for deep gray matter nuclei segmentation

Self-supervised pre-training based hybrid network for deep gray matter nuclei segmentation

Yang Deng Jiaxiu Xi Zhong Chen Lijun Bao

Magnetic Resonance Letters2026，Vol.6Issue(1)：P.53-65,13.

Magnetic Resonance Letters2026，Vol.6Issue(1)：P.53-65,13.DOI:10.1016/j.mrl.2025.200226

Self-supervised pre-training based hybrid network for deep gray matter nuclei segmentation

Yang Deng ¹Jiaxiu Xi ²Zhong Chen ³Lijun Bao²

作者信息

1. Institute of Artificial Intelligence,Xiamen University,Xiamen,361000,China
2. Department of Electronic Science,Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance,Xiamen University,Xiamen,361000,China
3. Department of Electronic Science,Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance,Xiamen University,Xiamen,361000,China Institute of Artificial Intelligence,Xiamen University,Xiamen,361000,China
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摘要

关键词

Deep gray matter nuclei segmentation/Self-supervised learning/Rotation prediction/Masked feature reconstruction/Transformer

分类

信息技术与安全科学

引用本文复制引用

Yang Deng,Jiaxiu Xi,Zhong Chen,Lijun Bao..Self-supervised pre-training based hybrid network for deep gray matter nuclei segmentation[J].Magnetic Resonance Letters,2026,6(1):P.53-65,13.

基金项目

supported in part by the National Natural Science Foundation of China under Grant 62071405 （）

the National Natural Science Foundation of China under Grant 12175189. （）

Magnetic Resonance Letters

ISSN：2097-0048

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