3D Shape Analysis for Computational Anatomy

计算解剖学的 3D 形状分析

• 批准号: 8038288
• 负责人: MICHAEL I MILLER
• 金额: $ 44.97万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-05-01 至 2013-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8038288
• 关键词: 15 year old; Adolescent; Age; Age of Onset; Algorithms; Amygdaloid structure; Appearance; Area; Atlases; Automobile Driving; Bio-Base; Biological; Biological Markers; Biomedical Informatics Research Network; Categories; Characteristics; Child; Childhood; Classification; Clinical; Communities; Computer software; Data; Dependency; Detection; Discriminant Analysis; Disease; Disease Clusterings; Elements; Etiology; Female; Funding; Future; Goals; Gold; Hippocampus (Brain); Image; Imagery; Individual; Institution; Investigation; Label; Language; Libraries; Major Depressive Disorder; Maps; Measures; Medical; Medical Imaging; Mental Depression; Mental disorders; Methods; Metric; Modeling; Mood Disorders; Motivation; Nerve Degeneration; Neurosciences; Nursery Schools; Onset of illness; Output; Population; Principal Component Analysis; Probability; Process; Psychiatry; Public Health; Reading; Request for Applications; Research; Research Project Grants; Role; Sampling; Science; Shapes; Signal Transduction; Slice; Staging; Statistical Methods; Statistical Models; Stream; Structure; Students; Surface; System; Testing; Time; To specify; Twin Multiple Birth; Twin Studies; Universities; Variant; Visual; Washington; Work; Writing; base; computational anatomy; computer science; data modeling; digital; disorder control; early onset; flexibility; follow-up; high risk; indexing; male; morphometry; multidisciplinary; neuroimaging; neuropsychiatry; open source; population based; shape analysis; statistics; tool; vector; young adult

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.

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下解剖结构的变异性。困难在于解剖子结构的复杂性和受试者之间的巨大差异。建议开发一个开源管道，用于对来自解剖结构群体的解剖变化进行3D统计形状分析。总体目标是将3D Slicer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，从而使更广泛的神经成像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的形状变形矢量生成的几个CA方法，如大变形几何度量映射（LDDMM）在成像科学中心在约翰霍普金斯大学和有限元方法的变形注册（FEMDR）中使用的ITK。这将允许形状向量被全局度量分类器分析用于对疾病形状进行分类，并被高斯随机场（GRF）模型分析用于定位疾病的形状变化。这两种方法将被统一，以提供一个新的度量分类器的基础上产生的数据GRF。在最后阶段，假设检验将用于将全局度量分类与局部形状变化相关联。第二个目标是构建形状向量分析所需的解剖图谱。这些图谱将由圣刘易斯的华盛顿大学在重性抑郁症（MDD）的神经影像学研究中已经获得的儿童、青少年和年轻人群体中的分段海马和杏仁核结构产生。作为一个主要的公共卫生负担，MDD提供了生物测试平台的管道，从概率地图集将产生。第三个目标是通过利用3D Slicer软件的强大功能和灵活性以及NA-MIC、Kitware等开发的ITK库，将软件库与管道集成。第四个目标是实现3D Slicer中形状向量分析的可视化模块。第五个目标是实现独立于3D Slicer的医学现实标记语言（MRML）的独立版本。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。公共卫生部门：这种基于强大的计算解剖学和计算机科学软件的多学科，多机构的研究具有很强的潜力，可以显着增加神经发育和神经退行性疾病的病因。驱动生物学动机来自于对早发性抑郁症的补充性神经影像学研究，因为抑郁症给全世界带来了相当大的公共卫生负担。早发性疾病的重要性增加，结合应用到基于人群的样本的双胞胎对出现作为一个有吸引力的模型，为神经科学界的统计形状分析软件。