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

Brain Informatics

program collaborators for Environmental influences on Child Health Outcomes¹  Andrew Laine²  Xuzhe Zhang²  Fateme Sadat Haghpanah³  Yun Wang⁴  Jonathan Posner⁴  Alexis Maddocks⁵  Katie Santamaria⁶  Elizabeth Bruno⁶  Catherine Monk⁶  Natalie Aw⁶  Cristiane S. Duarte⁶  Gabriela Koch da Costa Aguiar Alves⁶ 

[1] ;Department of Biomedical Engineering, Columbia University;Department of Computer Science, University Of Toronto;Department of Psychiatry and Behavioral Sciences, Duke University;Department of Radiology, Columbia University;New York State Psychiatric Institute;

关键词: Deep learning;    Segmentation;    Infant neuroimaging;    Convolutional neural networks;    Hippocampus;    Amygdala;   

DOI  :  10.1186/s40708-022-00161-9

来源: DOAJ

【 摘 要 】

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 a large, public infant MRI dataset (n = 473) and the transfer-learning technique to first pre-train a deep convolutional neural network model on two limbic structures: amygdala and hippocampus. Then we used a leave-one-out cross-validation strategy to fine-tune the pre-trained model and evaluated it separately on two independent datasets with manual labels. We term this new approach the Infant Deep learning SEGmentation Framework (ID-Seg). ID-Seg performed well on both datasets with a mean dice similarity score (DSC) of 0.87, a mean intra-class correlation (ICC) of 0.93, and a mean average surface distance (ASD) of 0.31 mm. Compared to the Developmental Human Connectome pipeline (dHCP) pipeline, ID-Seg significantly improved segmentation accuracy. In a third infant MRI dataset (n = 50), we used ID-Seg and dHCP separately to estimate amygdala and hippocampus volumes and shapes. The estimates derived from ID-seg, relative to those from the dHCP, showed stronger associations with behavioral problems assessed in these infants at age 2. In sum, ID-Seg consistently performed well on two different datasets with an 0.87 DSC, however, multi-site testing and extension for brain regions beyond the amygdala and hippocampus are still needed.

【 授权许可】

Unknown