Computational Modeling of Anatomical Shape Distributions

描述(由申请人提供)：从医学图像中分割出详细的、患者特定的模型可以为手术计划和导航提供宝贵的帮助。当遇到细微的灰度边界时，现有的分割方法往往会出错。通过引导结构朝向与图像信息一致的最可能的形状，添加结构的预期形状和形状的法线变化范围的知识可以极大地改进分割。由此产生的节段可以用来计划外科手术，当注册到患者身上时，可以提供围绕关键结构的导航指导。许多神经系统疾病，如阿尔茨海默氏症、精神分裂症和胎儿生长受限，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进展，以及开发将病例分类为患病或正常类别的方法，1需要捕捉人群之间形状分布的差异的方法。我们的目标是开发和验证从图像中学习解剖形状及其可变性的简明表示的方法，形状分布建模将通过将搜索偏向更可能的形状来改进分割算法。它还将使在种群研究中基于形状的定量分析成为可能，其中成像被用于研究种群之间的解剖差异，以及种群内的变化，例如随着年龄的变化。这项研究建立在已有的分割和形状分析方法的基础上，使用计算机视觉和机器学习的工具来研究种群研究中的形状表示、基于形状的分割和形状分析问题。我们计划进一步开发这些方法，并与我们的合作者一起在几个不同的应用中验证它们，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病研究。

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.