Mindboggling Shape Analysis and Identification

描述(由申请人提供)：本方案的具体目标是通过从人脸识别文献派生的贝叶斯框架，基于对其形状的几何和参数分析，自动识别/匹配大脑特征。Mindbogble是一个免费提供的用于执行自动解剖脑标记的软件包，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Mindbogger，根据这些概率匹配自动标记整个大脑。这些解剖标签为交流、分类和分析生物医学研究数据提供了一致、方便和有意义的方式。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的体积和形状分析，以及对例如fMRI或PET活动数据的区域特定分析。有两个长期的研究目标：(1)建立一种一致的、自动的、快速的脑标记方法，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，它们在健康受试者和患者中的变化和协变，以及它们与微结构、连通性、生理和功能活动的关系，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)，目的是识别与这些形状相对应的大脑结构，并表征其在大脑中的形态变化。用于分割这些骨架的Mindbogble算法。形状的几何分析将包括粗略的形状描述符，例如两个共同配准的形状之间的平均距离、它们的体积、重叠程度等。参数分析将使用由活动表面模型得出的定量形状差异度量。形状之间的这些相似性测量将被应用于人工标记的大脑数据的大数据集，并被合并到贝叶斯框架中，以估计与特定大脑结构相对应的给定形状的概率。图像处理将需要对大脑皮质皱褶进行骨架分析，同时将为研究和教育目的手动标记大脑的数据集，以及从该数据集得出的单个大脑形态可变性的数据库。公共卫生相关性：自动表征大脑结构的形状，并以准确、快速和一致的方式标记大脑图像数据的解剖结构，对于有意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、方便和有意义的方式来交流、分类和分析生物医学研究数据。临床研究应用包括随着时间的推移、跨条件和跨患者或健康受试者组的大脑区域的体积和形状分析，以及从这些区域获得的fMRI或PET活动数据的分析。

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.