ID-Seg: an infant deep learning-based segmentation framework to improve limbic structure estimates

ID-Seg: an infant deep learning-based segmentation framework to improve limbic structure estimates

Abstract Infant brain magnetic resonance imaging (MRI) is a promising approach for studying early neurodevelopment. However, segmenting small regions such as limbic structures is challenging due to their low inter-regional contrast and high curvature. MRI studies of the adult brain have successfully...

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Bibliographic Details
Main Authors: Yun Wang, Fateme Sadat Haghpanah, Xuzhe Zhang, Katie Santamaria, Gabriela Koch da Costa Aguiar Alves, Elizabeth Bruno, Natalie Aw, Alexis Maddocks, Cristiane S. Duarte, Catherine Monk, Andrew Laine, Jonathan Posner, program collaborators for Environmental influences on Child Health Outcomes
Format: Article
Language:English
Published: SpringerOpen 2022-05-01
Series:Brain Informatics
Subjects:
Deep learning
Segmentation
Infant neuroimaging
Convolutional neural networks
Hippocampus
Amygdala
Online Access:https://doi.org/10.1186/s40708-022-00161-9

Internet

https://doi.org/10.1186/s40708-022-00161-9

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