Early post-natal period brain magnetic resonance imaging (MRI) is becoming a common non-invasive approach to characterize the impact of prenatal exposures on neurodevelopment and to investigate early biomarkers for risk. Limbic structures are particular of interest in psychiatric disorder related research. Despite the promise of infant neuroimaging and the success of initial infant MRI studies, assessing limbic structure and function remains a significant challenge due to low inter-regional intensity contrast and high curvature (e.g. hippocampus). Of note, the agreement between existing automatic techniques and manual segmentation remains either untested or poor particularly for the amygdala and hippocampus. In this work, we developed an accurate (based on three segmentation evaluation metrics), reliable and efficient infant deep learning segmentation framework (ID−Seg) to address the aforementioned challenges. Specifically, we leveraged a large dataset of 473 infant MRI scans to train ID−Seg and then evaluated ID−Seg performance on internal (n=20) and external datasets (n=10) with manual segmentations. Compared with a state-of-the-art segmentation pipeline, we demonstrated that ID−Seg significantly improved the segmentation accuracy of limbic structures (hippocampus and amygdala) in newborn infants. Moreover, in a small, proof−of−concept analysis, we found that ID-Seg derived morphometric measures yield strong brain−behavior associations. As such, our ID-Seg may improve our capacity to efficiently measure MRI−based brain features relevant to neuropsychological development, and ultimately advance the success of quantitative analyses on large-scale datasets.
DEEP-AD: A Multimodal Temporal Video Segmentation Framework for Online Video Advertising
