AmygdalaGo-BOLT for boundary-aware segmentation of the human amygdala.
Authors
Affiliations (8)
Affiliations (8)
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Faculty of Psychology, Beijing Normal University, Beijing 100875, China.
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China.
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Faculty of Psychology, Beijing Normal University, Beijing 100875, China.
- Faculty of Psychology, Beijing Normal University, Beijing 100875, China.
- School of Psychology, Capital Normal University, Beijing 100048, China.
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing 100053, China. Electronic address: [email protected].
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Faculty of Psychology, Beijing Normal University, Beijing 100875, China; Developmental Population Neuroscience Research Center, McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; National Basic Science Data Center, Beijing 100190, China. Electronic address: [email protected].
- School of Physics, Zhejiang University, Hangzhou 310058, China; State Key Laboratory of Brain-Machine Intelligence, Zhejiang University, Hangzhou 310027, China. Electronic address: [email protected].
Abstract
Tracing the boundaries of the amygdala from brain images remains a major challenge in human neuroscience. Although large-scale neuroimaging studies increasingly collect thousands of scans to investigate structural development in children and adolescents, reliable segmentation of the amygdala is difficult due to its small size and complex morphology-particularly in pediatric populations. To address this, we developed AmygdalaGo-BOLT, a boundary-aware deep learning model specifically designed for amygdala segmentation. The model was trained and validated on 1,086 manually labeled pediatric MRI scans, with independent datasets used to assess generalizability. It integrates multiscale feature extraction, spatial priors, and self-attention mechanisms within a compact encoder-decoder architecture to enhance boundary detection. Across imaging centers and age groups, AmygdalaGo-BOLT demonstrates strong agreement with expert manual annotations, while substantially improving efficiency and accuracy relative to existing tools. This enables robust and scalable analysis of amygdala morphology in population neuroscience studies where manual tracing is impractical.