A Joint Segmentation and Registration Framework for Early Brain Development Study

 描述(申请人提供)：生命的头两年是出生后大脑发育最活跃的阶段，也可能是最关键的阶段。在基于图像的研究中，准确描述结构变化的能力对于探索早期大脑发育和早期发现神经发育障碍非常关键，这高度依赖于图像分割和配准技术。然而，无论是婴儿图像分割还是配准，如果独立展开，都会遇到比成人大脑更多的挑战，因为婴儿的外表发生了戏剧性的变化，大脑发育迅速。幸运的是，图像分割和配准可以通过使用从大量完整的纵向数据(2周、3个月、6个月、9个月、1岁和2岁)学习的生长轨迹(时间对应)来帮助对方克服困难。具体地说，我们将开发一个联合分割和配准框架来确定每个图像点的组织类型，并同时找到任意两个年龄差距较大的婴儿脑图像之间的变形路径(目标1)。初步结果表明，这种方法有显著的好处。在全面评估其在大量婴儿数据上的表现后，我们将把我们的联合分割和配准方法打包成一个软件包，并免费发布给社区(Aim 2)，就像我们对其他已被下载超过10,000次的软件包所做的那样。考虑到图像分割和配准在计算解剖学领域的重要性，这一前沿技术也将对许多正在进行的早期脑发育研究非常有用。

 DESCRIPTION (provided by applicant): 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.