This application addresses broad Challenge Area (06): Enabling Technologies, and specific Challenge Topic 06-MH-103: New Technologies for Neuroscience Research. The goal of this project is to develop fully automatic, accurate neonatal brain segmentation methods, to facilitate understanding of normal brain development and also future study of neurodevelopmental disorders in the first two years of life. Brain development in this period is the most dynamic phase of postnatal brain development with rapid structural and functional growth. Although there has been a great deal of recent interest in childhood and adolescent brain development, very little is known about human brain development in the first two years, due to the challenges in brain image acquisition and analysis of children at this age. Therefore, there is a knowledge gap that needs to be filled in understanding early brain development. In UNC-Chapel Hill, with a special environment for imaging young pediatric subjects, including a dedicated team of study coordinators, MR technologists, and nurses, UNC investigators have pioneered using MRI to allow for longitudinal investigation of changes in the gray and white matter composition of cortical and subcortical structures in the first two years of life. Specially, with four NIH-funded studies focusing on early brain development, a large set of longitudinal images (including 410 neonatal images at two weeks, 185 images at one-year-old and 126 images at two-year-old) have been acquired. To process data of this scale, automatic tools that require minimal human intervention are highly needed for better understanding and study of early brain development in the first two years of life. However, due to low tissue contrast, poor image quality, and dynamic changes of tissue intensity with WM myelination, it is very challenging to accurately segment brain tissues from neonatal MR images. Moreover, the existing segmentation algorithms developed for adult or even pediatric brain images fail to segment neonatal brain images satisfactorily. We propose to address this challenging issue by developing a novel neonatal segmentation method by taking advantage of the unique longitudinal datasets acquired in our four NIH-funded projects. We also propose to develop additional atlas-based neonatal brain segmentation method for the neonatal images with missing longitudinal data, which is typical in all longitudinal studies. The development of these neonatal brain segmentation methods will support not only the early brain development studies conducted in UNC, but also other similar projects currently conducted in other institutes, since our developed algorithms will be made freely available to the research community, as we did with our HAMMER registration algorithm (www.nitrc.org/projects/hammer/), which is one of the top downloaded tools in NITRC. With these developed methods, we will eventually be able to draw critical, previously unknown information about early brain development, which will have direct impact on our understanding of normal brain development and will provide the basis for future study and early detection of neurodevelopmental disorders in this age group.
This project aims at addressing a challenging problem of neonatal MR brain image segmentation by developing, testing, and evaluating two fully automatic, accurate neonatal segmentation algorithms. The development of these tools will help the understanding of early brain development and future studies of neurodevelopmental disorders in the first two years of life. Furthermore, it will help fill the knowledge gap of early brain development in this period.
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