Sparse methods on manifolds of images and shapes for significant anatomy detection linked to disease

针对多种图像和形状的稀疏方法，用于与疾病相关的重要解剖学检测

• 批准号: RGPIN-2016-04671
• 负责人: Cobzas, Dana
• 金额: $ 1.89万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=681677
• 关键词: Sparse; methods; manifolds; images; shapes

Medical imaging plays a key role in modern medicine, being part of many diagnoses and treatments. While imaging hardware and acquisition methods have seen a tremendous development in the last decade, this is not matched by advances in image processing software. Therefore, in clinical practice, much image processing work is either not performed at all, or performed manually due to inadequate software. To realize the full benefit of these new medical imaging methods, new and better image analysis methods need to be developed.******My current and planned research focuses on developing modern medical image analysis methods for studying anatomical and physiological variability in humans due to normal aging or disease. Such studies involve computational methods that extract and interpret image information to identify and quantify change. Our methods make use of anatomical similarity between humans parts and organs (such as ventricles or hippocampus among brain structures) to define a shape model. This shape model is defined in a continuous way and captures both global and local variability between individuals. We develop methods to statistically study disease-induced changes in anatomical shapes. This will provide a way to better understand the influence of certain diseases. ******This proposal introduces both theoretical advances of main medical image analysis methods (segmentation, shape analysis, multidimensional statistics) as well as practical modern solutions for a brain imaging study through our collaborative medical and biomedical collaborators. In addition, it opens the opportunity of training graduate students in an interesting multidisciplinary environment.

医学成像在现代医学中起着关键作用，是许多诊断和治疗的一部分。虽然成像硬件和采集方法在过去十年中取得了巨大的发展，但图像处理软件的进步并不匹配。因此，在临床实践中，许多图像处理工作要么根本不执行，要么由于软件不足而手动执行。为了实现这些新的医学成像方法的全部益处，需要开发新的和更好的图像分析方法。我目前和计划的研究重点是开发现代医学图像分析方法，用于研究由于正常衰老或疾病而导致的人体解剖和生理变化。这些研究涉及提取和解释图像信息以识别和量化变化的计算方法。 我们的方法利用人体部位和器官之间的解剖学相似性（例如大脑结构中的脑室或海马）来定义形状模型。该形状模型以连续的方式定义，并捕获个体之间的全局和局部变化。我们开发的方法来统计研究疾病引起的解剖形状的变化。这将为更好地了解某些疾病的影响提供一种方法。** 该提案介绍了主要医学图像分析方法（分割，形状分析，多维统计）的理论进展以及通过我们的合作医学和生物医学合作者进行脑成像研究的实用现代解决方案。此外，它打开了一个有趣的多学科环境中培养研究生的机会。