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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=644067
• 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. ***The research program also includes the development of new fast multi-modal image registration techniques for time-sensitive domains (e.g. image-guided neurosurgery), where, by embedding learned context-specific, probabilistic feature/pixel selection into the registration strategy, we hope to show significant speedups without compromising accuracy. Probabilistic techniques will be developed for the analysis of neurological images, focusing on learning the variability of cortical folding patterns in different sub-populations (e.g. age, IQ) and to automatically discover biomarkers of neurological disorders, such as MS and Alzheimer's patients. This will lead to innovations in terms of robust feature learning and clustering in contexts with large variability.

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，这些问题应用于特别具有挑战性的医学成像环境中，例如神经病学和神经外科中的问题，这些问题有可能在鲁棒性，准确性或速度方面取得显着进步。首先，新的复杂的，多层次的概率图形模型将被设计用于各种各样的开放和困难的病理检测和分割任务在医学成像领域。新的，高层次的，不规则的网格（即非格子）随机场将被开发，以适当地表示上下文（空间和纹理）信息。 推断将跨越体素、病变和上下文水平随机场进行，直到收敛。为这些领域开发新的图形模型将需要从流行的邻域模型出发，在节点和边缘信息的适当模型的设计方面进行创新，以及适当的推理技术。除了对机器学习、医学图像分析和计算机视觉的理论贡献外，所产生的模型还应显著改善真实的临床领域中各种困难和重要的病理检测和分割任务，例如多发性硬化病变分割、脑肿瘤子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最大数据集，这些数据集包括真实的、多中心、临床试验、手动注释的患者MRI图像，以测试这些方法。* 该研究计划还包括为时间敏感领域（例如图像引导神经外科手术）开发新的快速多模态图像配准技术，通过将学习到的特定于上下文的概率特征/像素选择嵌入配准策略中，我们希望在不影响准确性的情况下显着加速。 将开发用于神经图像分析的概率统计技术，重点是学习不同亚群（例如年龄，智商）中皮质折叠模式的变异性，并自动发现神经系统疾病的生物标志物，如MS和阿尔茨海默氏症患者。这将导致在具有较大可变性的上下文中的鲁棒特征学习和聚类方面的创新。