The first two years of life is the most dynamic and perhaps the most critical phase of postnatal brain development. The ability to accurately characterize structure changes is very critical for the exploration of early brain development and early detection of neurodevelopmental disorder in imaging-based studies, which highly relies on image segmentation and registration techniques. However, either infant image segmentation or registration, if deployed independently, encounters more challenges than adult brains due to the dramatic appearance change and rapid brain development. Fortunately, image segmentation and registration can assist each other to overcome the difficulties by using the growth trajectories (temporal correspondences) learned from a large amount of complete longitudinal data (at 2 weeks, 3 months, 6 months, 9 months, 1 year and 2 years of age) with multi-modality images (T1, T2, and DTI) collected in UNC-CH. Specifically, we will develop a joint segmentation and registration framework to determine the tissue type for each image point and simultaneously find the deformation pathway between any two infant brain images with significant age gap (Aim 1). Preliminary results demonstrate significant benefits of this approach. After comprehensively evaluating its performance on a large number of infant data, we will package our joint segmentation and registration approach into a software package and release it freely to the community (Aim 2), as we have done with our other software packages that have been downloaded for more than 10,000 times. Considering the importance of image segmentation and registration in computational anatomy area, this cutting-edge technique will be also very useful for many ongoing early brain development studies.
This proposal aims to develop an efficient computational anatomy approach to deal with the difficult tissue segmentation and registration of infant brain images in the first years of life. Specifically, to overcome the issues of dynamic appearance changes and spatially-varied development, we propose a joint image segmentation and registration framework to simultaneously determine the tissue type in each image point and further find the deformation pathway between any two infant images at different development stages. Considering the importance of image segmentation and registration in computational anatomy area, this cutting-edge technique will be also very useful for many ongoing early brain development studies.
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