Computational Modeling of Anatomical Shape Distributions

解剖形状分布的计算模型

• 批准号: 7560409
• 负责人: William Eric Grimson
• 金额: $ 28.05万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-02-15 至 2011-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7560409
• 关键词: Affect; Age; Aging; Algorithms; Alzheimer' s Disease; Anatomy; Area; Back; Computer Simulation; Computer Vision Systems; Data; Development; Disease; Disease Progression; Evaluation; Fetal Growth Retardation; Goals; Gold; Human; Image; Imagery; Intuition; Knowledge; Learning; Machine Learning; Measures; Medical; Methods; Modeling; Neonatal; Normal Range; Operative Surgical Procedures; Patients; Population; Population Study; Process; Research; Schizophrenia; Shapes; Statistical Distributions; Structure; System; Techniques; Testing; Training; Variant; base; computer studies; disease classification; feeding; improved; neonate; nervous system disorder; normal aging; novel; shape analysis; tool

DESCRIPTION (provided by applicant): Segmentation of detailed, patient-specific models from medical imagery can provide invaluable assistance for surgical planning and navigation. Current segmentation methods often make errors when confronted with subtle intensity boundaries. Adding knowledge of expected shape of a structure, and the range of normal variations in shape, can greatly improve segmentation, by guiding it towards the most likely shape consistent with the image information. The resulting segmentations can be used to plan surgical procedures, and when registered to the patient, can provide navigational guidance around critical structures. Many neurological diseases, such as Alzheimer's, schizophrenia, and Fetal Growth Restriction, affect the shape of specific anatomical areas. To understand the development and progression of these diseases, as well as to develop methods for classifying instances into diseased or normal classes, 1 needs methods that capture differences in shape distributions between populations. Our goal is to develop and validate methods for learning from images concise representations of anatomical shape and its variability, Modeling shape distributions will improve segmentation algorithms by biasing the search towards more likely shapes. It will also enable quantitative analysis based on shape in population studies, where imaging is used to study differences in anatomy between populations, as well as changes within a population, for example with age. The proposed research builds on prior methods for segmentation and shape analysis, using tools from computer vision and machine learning applied to questions of shape representation, shape based segmentation and shape analysis for population studies. We plan to further develop the methods and to validate them with our collaborators in several different applications, including surgical planning, neonatal imaging and image-based studies of aging and Alzheimer's disease.

描述（由申请人提供）：对医学图像的详细，特定于患者的模型进行分割可以为手术计划和导航提供宝贵的帮助。当前的分割方法面对微妙的强度边界时通常会出现错误。通过引导与图像信息一致的最有可能形状的形状，增加了结构的预期形状以及形状正常变化的范围。最终的分割可用于计划外科手术程序，并在注册患者时可以围绕关键结构提供导航指导。许多神经系统疾病，例如阿尔茨海默氏症，精神分裂症和胎儿生长限制，都会影响特定解剖区域的形状。为了了解这些疾病的发展和发展，以及开发将实例分类为患病或正常类别的方法，1种需要捕获种群形状分布差异的方法。我们的目标是开发和验证从图像的解剖形状及其可变性的图像简明表示的方法，建模形状分布将通过将搜索偏向更可能的形状来改善分割算法。它还将基于人群研究中的形状进行定量分析，其中成像用于研究人群之间的解剖结构差异以及人群中的变化，例如随着年龄的增长。拟议的研究基于用于分割和形状分析的先前方法，使用计算机视觉和机器学习的工具应用于形状表示问题，基于形状的分割和形状分析，用于人群研究。我们计划进一步开发这些方法，并在几种不同的应用中与我们的合作者进行验证，包括外科计划，新生儿成像和基于图像的衰老和阿尔茨海默氏病研究。