Neonatal Brain Segmentation

描述（由申请人提供）：本申请涉及广泛的挑战领域（06）：使能技术，以及具体的挑战主题06- mh -103：神经科学研究的新技术。该项目的目标是开发全自动、准确的新生儿大脑分割方法，以促进对正常大脑发育的理解，以及未来对生命最初两年神经发育障碍的研究。这一时期是出生后大脑发育最活跃的阶段，大脑结构和功能发育迅速。尽管最近人们对儿童和青少年的大脑发育有很大的兴趣，但由于对这个年龄段儿童的大脑图像采集和分析存在挑战，人们对头两年的大脑发育知之甚少。因此，在了解早期大脑发育方面，有一个知识缺口需要填补。在北卡罗来纳大学教堂山分校，有一个特殊的环境为年轻的儿科受试者成像，包括一个专门的研究协调员团队，核磁共振技术专家和护士，北卡罗来纳大学的研究人员率先使用核磁共振成像，允许纵向调查大脑皮层和皮层下结构的灰质和白质组成在生命的头两年的变化。特别的是，通过nih资助的四项关于早期大脑发育的研究，获得了大量的纵向图像（包括410张两周新生儿的图像，185张一岁婴儿的图像和126张两岁婴儿的图像）。为了更好地理解和研究生命最初两年的早期大脑发育，处理这种规模的数据，非常需要人工干预最少的自动工具。然而，由于组织对比度低、图像质量差以及WM髓鞘形成过程中组织强度的动态变化，从新生儿MR图像中准确分割脑组织是非常具有挑战性的。此外，现有的针对成人甚至儿童脑图像的分割算法都不能很好地分割新生儿脑图像。我们建议通过开发一种新的新生儿分割方法来解决这个具有挑战性的问题，该方法利用了我们在四个nih资助的项目中获得的独特的纵向数据集。我们还建议开发额外的基于地图集的新生儿大脑分割方法，用于缺失纵向数据的新生儿图像，这在所有纵向研究中都是典型的。这些新生儿大脑分割方法的发展不仅将支持在UNC进行的早期大脑发育研究，也将支持目前在其他研究所进行的其他类似项目，因为我们开发的算法将免费提供给研究社区，就像我们的HAMMER注册算法（http://www.nitrc.org/projects/hammer/）一样，HAMMER注册算法是NITRC下载最多的工具之一。有了这些成熟的方法，我们最终将能够得出关键的，以前未知的早期大脑发育信息，这将对我们对正常大脑发育的理解产生直接影响，并将为未来的研究和早期发现这一年龄组的神经发育障碍提供基础。

DESCRIPTION (provided by applicant): 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 (http://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. PUBLIC HEALTH RELEVANCE: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.