BRAIN IMAGE SEGMENTATION, COMPARISON, AND MEASUREMENT

脑图像分割、比较和测量

• 批准号: 6394753
• 负责人: LAWRENCE H. STAIB
• 金额: $ 16.98万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-30 至 2004-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6394753
• 关键词: autism; bioimaging /biomedical imaging; biomedical automation; brain disorder diagnosis; brain imaging /visualization /scanning; clinical research; computer program /software; computer system design /evaluation; diagnosis design /evaluation; human data; magnetic resonance imaging; mathematical model; mental disorder diagnosis; method development; model design /development; neuroanatomy; neurosciences; pathologic process; posttraumatic stress disorder; statistics /biometry

DESCRIPTION (Adapted from applicant's abstract): Technologies for high resolution structural imaging of the brain provide neuroscientists with tremendous opportunities for measuring the brain quantitatively and accurately. There is a need, however, for reliable true inter-subject comparison and accurate automated region-of-interest definition. Limitations in these areas have impeded progress in neuroscience in the understanding of normal and abnormal brain structure and in the characterization and treatment of an array of brain disorders, including posttraumatic stress disorder (PYSD) and autism, two conditions having subtle yet distinctive structural abnormalities. The objective of this work is the development of an automated computer system for the analysis of the brain from structural images from magnetic resonance (MR). The aim is to develop and validate methods and provide a tool to allow for accurate, detailed, inter-subject comparison of structure in the brain. Such methods would also be directly applicable to associated functional image. This work requires the refinement and development of segmentation and non-rigid registration methodologies. The limitations of current approaches to registration in terms of lack of detail and inaccuracy can be overcome by incorporating prior statistical information of shape for boundary finding and integrating this image-derived information using physical models for deformation. Characterizing neuroanatomic variation in a detailed probabilistic manner is crucial for constraining the necessary transformation and making it consistent and reliable. The resulting representation allows detailed morphologic description in terms of geometry and deformation. This methodology would also be applicable to comparison within an individual where there is structural change over time. This system will be validated with synthetic, phantom and real images from separately funded studies of PTSD and autism. The results are ideally suited for statistical analysis of structure. This system will allow the accurate and routine analysis and comparison of brain imaging studies to compute regional structural measures for improved understanding of the normal and abnormal brain and in the treatment of patients with brain disorders.

描述（改编自申请人的摘要）：高技术 大脑的分辨率结构成像为神经科学家提供了 巨大的机会来定量，准确地测量大脑。 但是，需要可靠的真实主体间比较和 准确的自动化区域定义。这些领域的局限性 在理解正常和 大脑结构异常以及阵列的表征和处理 脑疾病，包括创伤后应激障碍（PYSD）和自闭症， 两个条件具有微妙而独特的结构异常。这 这项工作的目的是开发自动化计算机系统 来自磁共振（MR）的结构图像对大脑的分析。 目的是开发和验证方法，并提供一种工具来允许 精确，详细的，主体间比较大脑中的结构。这样的 方法也将直接适用于关联的功能图像。这 工作需要细分和非刚性的细化和开发 注册方法。当前方法的局限性 从缺乏细节和不准确的情况下进行注册可以通过 合并边界发现形状的先前统计信息和 使用物理模型集成此图像衍生的信息 形变。表征详细概率中的神经解剖学变异 方式对于限制必要的转变至关重要 一致和可靠。结果表示允许详细 形态学描述在几何和变形方面。这种方法 也适用于在有个人的个人中的比较 随着时间的推移结构变化。该系统将通过合成验证， 来自PTSD和自闭症的单独资助研究的幻影和真实图像。这 结果非常适合结构的统计分析。这个系统 将允许精确，常规的分析和大脑成像的比较 计算区域结构措施的研究，以提高对 大脑正常和异常的大脑治疗 疾病。