Mindboggling Shape Analysis and Identification

令人难以置信的形状分析和识别

• 批准号: 7737395
• 负责人: Arno Klein
• 金额: $ 31.56万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7737395
• 关键词: Address; Algorithms; Anatomy; Atlases; Base of the Brain; Behavioral Genetics; Biomedical Research; Brain; Brain imaging; Brain region; Characteristics; Clinical; Clinical Research; Communities; Computer software; Data; Data Analyses; Data Set; Databases; Descriptor; Development; Environment; Evaluation; Face; Functional Magnetic Resonance Imaging; Goals; Human; Individual; Knowledge; Label; Literature; Magnetic Resonance Imaging; Manuals; Measures; Medial; Mental Health; Methods; Metric; Modeling; Morphology; Patients; Physiology; Positron-Emission Tomography; Probability; Process; Research; Research Design; Research Infrastructure; Research Personnel; Shapes; Skeleton; Structure; Surface; Time; Variant; Work; base; brain morphology; brain research; brain shape; brain size; image processing; interest; public health relevance; shape analysis

DESCRIPTION (provided by applicant): The specific aim of this proposal is to automatically identify/match brain features based on a geometric and parametric analysis of their shapes, by means of a Bayesian framework derived from the face recognition literature. Mindboggle, a freely available software package for performing automated anatomical brain labeling, will serve as the software infrastructure for implementing the Bayesian framework. The secondary aim is to further develop Mindboggle to automatically label an entire brain based on these probabilistic matches. These anatomical labels provide a consistent, convenient, and meaningful way to communicate, classify, and analyze biomedical research data. Clinical research applications of labeled regions include volumetric and shape analysis of brain regions over time or across conditions, and region-specific analysis of, for example, fMRI or PET activity data. There are two longterm research objectives: (1) establish a consistent, automatic, and fast method for labeling brains with an accuracy and precision comparable to that of manual labelers, and (2) obtain shape characteristics of anatomical regions, their variations and covariations in healthy subjects and patients, and their relationships to microstructure, connectivity, physiology, and functional activity for genetic, behavioral, developmental, and clinical research. Brain structures will be extracted from human brain MR image data and analyzed (described and compared) using geometrical and parametric approaches, for the purpose of identifying the brain structures to which the shapes correspond and characterizing their morphological variability across brains. Mindboggle algorithms for fragmenting these skeletons. Geometric analysis of shapes will include the gross shape descriptors such as mean distance between two coregistered shapes, their volumes, degree of overlap, etc. Parametric analysis will employ quantitative shape discrepancy metrics derived by an active surfaces model. These measures of similarity between shapes will be applied to a large dataset of manually labeled brain data and incorporated in a Bayesian framework for the purpose of estimating the probability of a given shape corresponding to a particular brain structure. Image processing will entail skeletonization of brain cortical folds combined with revised Additional contributions will be a dataset of manually labeled brains for research and educational purposes, and a database of individual brain morphological variability derived from this dataset. PUBLIC HEALTH RELEVANCE: Automatically characterizing the shapes of brain structures and labeling the anatomy of brain image data in an accurate, fast, and consistent manner is of immense value to clinical researchers interested in the application of brain imaging to mental health. Anatomical labels provide a consistent, convenient, and meaningful way to communicate, classify, and analyze biomedical research data. Clinical research applications include volume and shape analysis of brain regions over time, across conditions, and across groups of patients or healthy subjects, as well as analysis of fMRI or PET activity data acquired from these regions.

描述（由申请人提供）：本提案的具体目的是通过源自人脸识别文献的贝叶斯框架，基于对其形状的几何和参数分析，自动识别/匹配大脑特征。Mindboggle是一个免费的软件包，用于执行自动解剖脑标记，将作为实现贝叶斯框架的软件基础设施。第二个目标是进一步开发Mindboggle，根据这些概率匹配自动标记整个大脑。这些解剖标签提供了一种一致的、方便的、有意义的方式来交流、分类和分析生物医学研究数据。标记区域的临床研究应用包括脑区域随时间或不同条件的体积和形状分析，以及区域特异性分析，例如fMRI或PET活动数据。有两个长期的研究目标：(1)建立一种一致的、自动的、快速的方法来标记大脑，其准确性和精度与人工标记器相当；(2)获得健康受试者和患者解剖区域的形状特征，它们的变化和协变，以及它们与微观结构、连通性、生理学和功能活动的关系，用于遗传、行为、发育和临床研究。大脑结构将从人脑MR图像数据中提取，并使用几何和参数方法进行分析（描述和比较），目的是识别形状对应的大脑结构，并表征其在大脑中的形态变异性。令人难以置信的算法来分割这些骨架。形状的几何分析将包括总体形状描述符，如两个共注册形状之间的平均距离，它们的体积，重叠程度等。参数分析将采用由活动曲面模型导出的定量形状差异度量。这些形状之间的相似性度量将应用于人工标记的大脑数据的大型数据集，并纳入贝叶斯框架，以估计特定大脑结构对应的给定形状的概率。图像处理将需要对大脑皮质褶皱进行骨骼化，并结合修订后的数据集，另外的贡献将是一个用于研究和教育目的的人工标记的大脑数据集，以及一个来自该数据集的个体大脑形态变异数据库。公共卫生相关性：以准确、快速和一致的方式自动表征大脑结构的形状和标记大脑图像数据的解剖结构，对对脑成像在心理健康中的应用感兴趣的临床研究人员具有巨大的价值。解剖标签提供了一种一致的、方便的、有意义的方式来交流、分类和分析生物医学研究数据。临床研究应用包括脑区域随时间、不同条件、不同患者或健康受试者群体的体积和形状分析，以及从这些区域获得的fMRI或PET活动数据分析。