A diffusion-based statistical shape model for medical image analysis

用于医学图像分析的基于扩散的统计形状模型

• 批准号: 403055-2011
• 负责人: Cobzas, Dana
• 金额: $ 1.38万
• 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: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=573378
• 关键词: diffusion; based; statistical; shape; model

Medical imaging plays a key role in modern medicine, being part of many diagnoses and treatments. While the imaging hardware and acquisition methods have seen a tremendous development in the last decade, this is not matched by the image processing software. Therefore, in clinical practice, much image processing work is still 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. We propose a medical image analysis framework based on modern computer vision techniques. Our methods make use of anatomical similarity between humans parts and organs to define a shape model. This shape model is defined in a continuous way and captures both global and local variability between individuals. We will use this shape representation to develop robust medical imaging analysis methods and 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 processing methods (segmentation, registration, shape analysis) as well as practical modern solution for multiple sclerosis and liver cancer though our medical 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发挥着关键作用，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有了巨大的发展，但这是图像处理软件无法比拟的。 因此，在临床实践中，由于软件的不足，很多图像处理工作仍然是手工完成的。为了实现这些新的医学成像方法的全部好处，需要开发新的和更好的图像分析方法。 提出了一种基于现代计算机视觉技术的医学图像分析框架。我们的方法利用人体部分和器官之间的解剖学相似性来定义形状模型。该形状模型以一种连续的方式定义，并捕获个体之间的全局和局部可变性。我们将使用这种形状表示来开发健壮的医学成像分析方法，并对疾病引起的解剖形状的变化进行统计研究。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处理方法(分割、配准、形状分析)的理论进展，以及通过我们的医疗合作者提出的多发性硬化症和肝癌的实用现代解决方案。此外，它还开启了在一个有趣的多学科环境中培养研究生的机会。