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 applied deep learning techniques to segment limbic structures, and similar deep learning models are being leveraged for infant studies. However, these deep learning-based infant MRI segmentation models have generally been derived from small datasets, and may suffer from generalization problems. Moreover, the accuracy of segmentations derived from these deep learning models relative to more standard Expectation-Maximization approaches has not been characterized. To address these challenges, we leveraged

SUBMITTER: Wang Y 

PROVIDER: S-EPMC9148335 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature