Predicting infant cortical surface development using a 4D varifold-based learning framework and local topography-based shape morphing
Year: 2015
Type: article
Source: Medical Image Analysis
Authors: Islem Rekik, Gang Li, Weili Lin, Dinggang Shen
Institution: University of North Carolina at Chapel Hill
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: National Institutes of Health, National Institute on Aging, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Institute of Biomedical Imaging and Bioengineering, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: G1234639804, G1761222865, Early Brain Development in One and Two Year Olds, Early Brain Development in Twins, Neonatal Brain Segmentation, Infant Brain Measurement and Super-Resolution Atlas Construction, G2686953907, G2849260730, G3186634833, G3258782064, G4798393751, Quantifying Brain Abnormality by Multimodality Neuroimage Analysis, Longitudinal Mapping of Human Brain Development in the First Years of Life, Fast, Robust Analysis of Large Population Data, 4D Cortical Surface Analysis Tools for Study of Early Brain Development in Typical and High-risk Infants, G665197000, Development of Robust Brain Measurement Tools Informed by Ultrahigh Field 7T MRI, Characterization of Normal Brain Development Using Parallel MRI, Early Brain Development in Twins, G9000081443, G93323634
OpenAlex ID: W2191591598