3D Shape Analysis for Computational Anatomy

计算解剖学的 3D 形状分析

• 批准号: 7557962
• 负责人: MICHAEL I MILLER
• 金额: $ 48.54万
• 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/7557962
• 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; Figs - dietary; Funding; Future; Goals; Gold; Hippocampus (Brain); Image; Imagery; Individual; Institution; Investigation; Label; Language; Libraries; Major Depressive Disorder; Maps; Measures; Medical; Medical Imaging; 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; 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; depression; 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(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可变性。困难在于解剖结构的复杂性和不同学科之间的巨大差异。有人建议开发一个开源管道，用于从一组解剖结构中对解剖变异进行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在一起，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)。这将允许全局度量分类器分析在分类疾病形状时使用形状向量，并允许高斯随机场(GRF)模型分析在定位疾病中的形状变化时使用形状向量。这两种方法将被统一起来，以提供基于GRF生成的数据的新的度量分类器。在最后阶段，将使用假设检验来将全局度量分类与局部形状变化相关联。第二个目标是构建分析形状向量所需的解剖图谱。这些图谱将从圣路易斯华盛顿大学对严重抑郁障碍(MDD)的神经成像研究中已经获得的儿童、青少年和年轻人群体的分段海马和杏仁核结构中生成。作为一个主要的公共卫生负担，MDD为管道提供了生物试验台，从那里将生成概率地图集。第三个目标是通过利用由NA-MIC、Kitware和其他公司开发的3D Slicer软件和ITK库的功能和灵活性，将软件库与流水线集成。第四个目标是在3D切片器中实现形状向量分析的可视化模块。第五个目标是实现一个独立于3D Slicer的医学现实标记语言(MRML)的独立版本。这将使MRML的传播成为未来神经成像应用的标准格式。形状分析管道将在NA-MIC的赞助下用于精神疾病的神经成像研究。公共卫生评论：这项基于强大的计算解剖学和计算机科学软件的多学科、多机构的调查，具有显著增加神经发育和神经退行性疾病的病因学的强大潜力。鉴于抑郁症在世界范围内造成的相当大的公共健康负担，驱动生物学动机来自对早发性严重抑郁症的补充神经成像研究。早发性疾病的重要性增加，再加上双胞胎样本在人群中的应用，对于神经科学界的统计形状分析软件来说，似乎是一个有吸引力的模型。