Mindboggling Shape Analysis and Identification

令人难以置信的形状分析和识别

• 批准号: 7882529
• 负责人: Arno Klein
• 金额: $ 32.2万
• 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/7882529
• 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）：该提案的具体目标是通过源自人脸识别文献的贝叶斯框架，基于对大脑形状的几何和参数分析来自动识别/匹配大脑特征。 Mindboggle 是一个免费提供的软件包，用于执行自动解剖大脑标记，将作为实施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、方便且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随时间或跨条件对大脑区域进行体积和形状分析，以及对 fMRI 或 PET 活动数据等区域特定的分析。有两个长期研究目标：（1）建立一致、自动、快速的大脑标记方法，其准确度和精密度可与手动标记器相媲美；（2）获得解剖区域的形状特征及其变化​​和协变。健康受试者和患者，以及他们与遗传、行为、发育和临床研究的微观结构、连通性、生理学和功能活动的关系。将从人脑 MR 图像数据中提取大脑结构，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。用于分割这些骨架的令人难以置信的算法。形状的几何分析将包括总体形状描述符，例如两个配准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表面模型导出的定量形状差异度量。这些形状之间相似性的测量将应用于手动标记的大脑数据的大型数据集，并合并到贝叶斯框架中，以估计给定形状对应于特定大脑结构的概率。图像处理将需要对大脑皮层褶皱进行骨架化，并结合修订后的其他贡献将是用于研究和教育目的的手动标记大脑的数据集，以及源自该数据集的个体大脑形态变异性的数据库。公共健康相关性：以准确、快速和一致的方式自动表征大脑结构的形状并标记大脑图像数据的解剖结构，对于有兴趣将大脑成像应用于心理健康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、方便且有意义的方式来交流、分类和分析生物医学研究数据。临床研究应用包括对不同时间、不同条件、不同患者组或健康受试者的大脑区域的体积和形状进行分析，以及对从这些区域获取的功能磁共振成像或正电子发射断层扫描（PET）活动数据进行分析。