Probabilistic Inference in Computer Vision and Medical Imaging

计算机视觉和医学成像中的概率推理

• 批准号: RGPIN-2015-05471
• 负责人: Arbel, Tal
• 金额: $ 3.13万
• 依托单位: McGill University
• 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=590452
• 关键词: Probabilistic; Inference; Computer; Vision; Medical

The objectives of the multidisciplinary research program proposed are to develop new probabilistic formulations for problems in computer vision applied in particularly challenging medical imaging contexts, such as those presented in neurology and neurosurgery, where they have the potential to lead to significant advancements in terms of robustness, accuracy or speed over traditional techniques. First, new sophisticated, multi-level probabilistic graphical models will be devised for a wide variety of open and difficult pathology detection and segmentation tasks in the domain of medical imaging. New, high level, irregular grid (i.e. non-lattice) random fields will be developed to properly represent contextual (spatial and textural) information. Inference will iterate across voxel, lesion and context level random fields until convergence. Development of new graphical models for these domains will require departures from popular neighbourhood models, innovation in design of appropriate models for node and edge information, as well as appropriate inference techniques. In addition to theoretical contributions machine learning, medical image analysis and to computer vision, the resulting models should significantly improve a wide variety of difficult and important pathology detection and segmentation tasks in real, clinical domains, such as Multiple Sclerosis lesion segmentation, the segmentation of sub-classes of brain tumours, and breast cancer detection. Through collaborations with a local company, my research team will have access to the largest dataset of real, multicenter, clinical trial, manually annotated, patient MRI images of any medical image analysis group in the world in which to test the methods.

提出的多学科研究计划的目标是为计算机视觉中的问题开发新的概率公式，这些问题适用于特别具有挑战性的医学成像环境，例如神经病学和神经外科中提出的那些，在这些领域中，它们有可能导致在稳健性、准确性或速度方面比传统技术有显著进步。首先，将为医学成像领域中各种开放和困难的病理检测和分割任务设计新的复杂的、多层次的概率图形模型。将开发新的、高级别、不规则网格(即非晶格)随机场，以适当地表示上下文(空间和纹理)信息。推理将在体素、病变和上下文级随机场中迭代，直到收敛。为这些领域开发新的图形模型将需要与流行的邻域模型背道而驰，创新设计节点和边缘信息的适当模型，以及适当的推理技术。除了机器学习、医学图像分析和计算机视觉的理论贡献外，所产生的模型还应该显著改进实际临床领域中各种困难和重要的病理检测和分割任务，例如多发性硬化症病变分割、脑肿瘤亚类分割和乳腺癌检测。通过与当地一家公司的合作，我的研究团队将获得世界上任何医学图像分析小组的真实、多中心、临床试验、手动注释的患者MRI图像的最大数据集，以测试这些方法。