Vachet Clement, Yvernault Benjamin, Bhatt Kshamta, Smith Rachel G, Gerig Guido, Hazlett Heather Cody, Styner Martin
Department of Psychiatry, University of North Carolina at Chapel Hill, NC, USA.
Department of Psychiatry, University of North Carolina at Chapel Hill, NC, USA ; Carolina Institute for Developmental Disabilities, UNC-Chapel Hill, NC, USA.
Proc SPIE Int Soc Opt Eng. 2012 Mar 23;8317:831707-. doi: 10.1117/12.911504.
The corpus callosum (CC) is a structure of interest in many neuroimaging studies of neuro-developmental pathology such as autism. It plays an integral role in relaying sensory, motor and cognitive information from homologous regions in both hemispheres. We have developed a framework that allows automatic segmentation of the corpus callosum and its lobar subdivisions. Our approach employs constrained elastic deformation of flexible Fourier contour model, and is an extension of Szekely's 2D Fourier descriptor based Active Shape Model. The shape and appearance model, derived from a large mixed population of 150+ subjects, is described with complex Fourier descriptors in a principal component shape space. Using MNI space aligned T1w MRI data, the CC segmentation is initialized on the mid-sagittal plane using the tissue segmentation. A multi-step optimization strategy, with two constrained steps and a final unconstrained step, is then applied. If needed, interactive segmentation can be performed via contour repulsion points. Lobar connectivity based parcellation of the corpus callosum can finally be computed via the use of a probabilistic CC subdivision model. Our analysis framework has been integrated in an open-source, end-to-end application called CCSeg both with a command line and Qt-based graphical user interface (available on NITRC). A study has been performed to quantify the reliability of the semi-automatic segmentation on a small pediatric dataset. Using 5 subjects randomly segmented 3 times by two experts, the intra-class correlation coefficient showed a superb reliability (0.99). CCSeg is currently applied to a large longitudinal pediatric study of brain development in autism.
胼胝体（CC）是许多神经发育病理学（如自闭症）神经影像学研究中的一个重要结构。它在两个半球同源区域之间传递感觉、运动和认知信息方面发挥着不可或缺的作用。我们开发了一个框架，可实现胼胝体及其叶状细分的自动分割。我们的方法采用灵活傅里叶轮廓模型的约束弹性变形，是基于Szekely的二维傅里叶描述符的主动形状模型的扩展。从150多名受试者的大量混合样本中得出的形状和外观模型，在主成分形状空间中用复杂傅里叶描述符进行描述。使用MNI空间对齐的T1加权MRI数据，利用组织分割在正中矢状面上初始化CC分割。然后应用多步优化策略，包括两个约束步骤和一个最终的无约束步骤。如有需要，可通过轮廓排斥点进行交互式分割。最后可通过使用概率性CC细分模型计算基于叶连接性的胼胝体分割。我们的分析框架已集成到一个名为CCSeg的开源端到端应用程序中，该应用程序既有命令行界面，也有基于Qt的图形用户界面（可在NITRC上获取）。我们进行了一项研究，以量化在一个小型儿科数据集上半自动分割的可靠性。由两名专家对5名受试者进行3次随机分割，类内相关系数显示出极高的可靠性（0.99）。CCSeg目前应用于一项关于自闭症大脑发育的大型纵向儿科研究。
