3D Shape Analysis for Computational Anatomy

描述（由申请人提供）：计算解剖学（CA）的长期目标是创建算法工具，帮助基础和临床神经科学家分析不同尺度解剖结构的变异性。难点在于解剖亚结构的复杂性和不同学科之间的巨大差异。建议开发一个开源的管道，用于从一群解剖结构中对解剖变化进行三维统计形状分析。总体目标是将3D切片机应用程序和ITK软件库与生物医学信息学研究网络传播的统计形状分析管道集成在一起，从而使更广泛的神经影像学社区能够有效地分析疾病的解剖变异。第一个目标是标准化由几种CA方法生成的形状变形向量，例如约翰霍普金斯大学成像科学中心开发的大变形微分对称度量映射（LDDMM）和ITK中使用的可变形配准的有限元方法（FEMDR）。这将允许形状矢量被用于对疾病形状进行分类的全局度量分类器分析和用于定位疾病形状变化的高斯随机场（GRF）模型分析。将这两种方法统一起来，以GRF生成的数据为基础，提供一个新的度量分类器。在最后阶段，假设检验将用于关联全局度量分类与局部形状变化。第二个目标是构建形状矢量分析所需的解剖图谱。这些地图集将在华盛顿大学圣路易斯分校进行的重度抑郁症（MDD）神经影像学研究中，从已经获得的儿童、青少年和年轻人群体的海马和杏仁核结构中提取。作为一个主要的公共卫生负担，MDD为将从中生成概率图谱的管道提供了生物试验台。第三个目标是通过利用由NA-MIC， Kitware和其他人开发的3D切片器软件和ITK库的功能和灵活性，将软件库与管道集成。第四个目标是实现三维切片器中形状矢量分析的可视化模块。第五个目标是实现独立于3D切片器的独立版本的医学现实标记语言（MRML）。这将允许MRML作为未来神经成像应用的标准格式进行传播。在NA-MIC的支持下，形状分析管道将被传播用于精神疾病的神经影像学研究。公共卫生启示：这项多学科、多机构的研究，基于强大的计算解剖学和计算机科学软件，具有强大的潜力，可以显著增加神经发育和神经退行性疾病的病因。驱动的生物学动机来自早发性重度抑郁症的补充神经影像学研究，考虑到抑郁症在全球范围内的巨大公共卫生负担。早发性疾病的重要性增加，并结合对基于人口的双胞胎样本的应用，为神经科学社区的统计形状分析软件提供了一个有吸引力的模型。

DESCRIPTION (provided by applicant): The long term goal of Computational Anatomy (CA) is to create algorithmic tools that aid basic and clinical neuroscientists in the analysis of variability in anatomical structures at different scales. The difficulty is the complexity of anatomical substructures and the large variation across subjects. It is proposed to develop an open-source pipeline for 3D statistical shape analysis of anatomical variations from a population of anatomical structures. The overall aim is to integrate 3D Slicer application and ITK software library with the statistical shape analysis pipeline being disseminated by the Biomedical Informatics Research Network and thus enable the wider neuroimaging community to efficiently analyze anatomical variations in disease. The first aim is to standardize shape deformation vectors generated by several CA methods such as the Large Deformation Diffeomorphic Metric Mapping (LDDMM) developed at the Center for Imaging Science at Johns Hopkins University and the Finite Element Method for Deformable Registration (FEMDR) used in ITK. This will allow shape vectors to be used by both global metric classifier analysis in classifying diseased shapes and Gaussian Random Field (GRF) model analysis in localizing shape changes in disease. The two methods will be unified to provide a new metric classifier based on the data generated by GRF. In the final stage, hypothesis testing will be used to correlate global metric classification with localized shape changes. The second aim is to construct anatomical atlases needed for analysis of shape vectors. These atlases will be generated from segmented hippocampal and amygdala structures in already acquired populations of children, adolescents and young adults in neuroimaging studies of major depression disorder (MDD) at Washington University at St Louis. As a major public health burden, MDD provides the biological testbed for the pipeline from which probabilistic atlases will be generated. The third aim is to integrate the software libraries with the pipeline by leveraging the power and flexibility of the 3D Slicer software and ITK libraries developed by NA-MIC, Kitware and others. The fourth aim is to implement modules for visualization of the analysis of shape vectors in 3D Slicer. The fifth aim is to implement a stand-alone version of Medical Reality Markup Language (MRML) independent of 3D Slicer. This will allow for the propagation of MRML as a standard format for future neuroimaging applications. The shape analysis pipeline will be disseminated for use in neuroimaging studies of psychiatric disorders under the auspices of NA-MIC. PUBLIC HEALTH REVELANCE: This multidisciplinary, multi-institutional investigation, based on powerful computational anatomy and computer science software, has a strong potential to add significantly to the etiology of neurodevelopmental and neurodegeneration disorders. The driving biological motivation comes from complementary neuroimaging studies of early onset major depression disorder given the considerable public health burden of depression worldwide. The increased importance of early onset illness combined with the application to a population-based sample of twin pairs appears as an attractive model for statistical shape analysis software for the neuroscience community.