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Simultaneous and consistent labeling of longitudinal dynamic developing cortical surfaces in infants
Affiliation:1. Department of Radiology and BRIC, The University of North Carolina at Chapel Hill, USA;2. Athena Project-Team, INRIA Sophia Antipolis-Méditerranée, France;3. Center for Computational Medicine, LIAMA, Institute of Automation, Chinese Academy of Sciences, China;1. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA;2. Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China;3. Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
Abstract:The human cerebral cortex develops extremely dynamically in the first 2 years of life. Accurate and consistent parcellation of longitudinal dynamic cortical surfaces during this critical stage is essential to understand the early development of cortical structure and function in both normal and high-risk infant brains. However, directly applying the existing methods developed for the cross-sectional studies often generates longitudinally-inconsistent results, thus leading to inaccurate measurements of the cortex development. In this paper, we propose a new method for accurate, consistent, and simultaneous labeling of longitudinal cortical surfaces in the serial infant brain MR images. The proposed method is explicitly formulated as a minimization problem with an energy function that includes a data fitting term, a spatial smoothness term, and a temporal consistency term. Specifically, inspired by multi-atlas based label fusion, the data fitting term is designed to integrate the contributions from multi-atlas surfaces adaptively, according to the similarities of their local cortical folding with that of the subject cortical surface. The spatial smoothness term is then designed to adaptively encourage label smoothness based on the local cortical folding geometries, i.e., allowing label discontinuity at sulcal bottoms (which often are the boundaries of cytoarchitecturally and functionally distinct regions). The temporal consistency term is to adaptively encourage the label consistency among the temporally-corresponding vertices, based on their similarity of local cortical folding. Finally, the entire energy function is efficiently minimized by a graph cuts method. The proposed method has been applied to the parcellation of longitudinal cortical surfaces of 13 healthy infants, each with 6 serial MRI scans acquired at 0, 3, 6, 9, 12 and 18 months of age. Qualitative and quantitative evaluations demonstrated both accuracy and longitudinal consistency of the proposed method. By using our method, for the first time, we reveal several hitherto unseen properties of the dynamic and regionally heterogeneous development of the cortical surface area in the first 18 months of life.
Keywords:Cortical surface  Parcellation  Longitudinal analysis  Infant  Early brain development
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