Computational Methods for Medical Image Interpretation

医学图像解释的计算方法

• 批准号: RGPIN-2015-06795
• 负责人: Hamarneh, Ghassan
• 金额: $ 3.64万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=614370
• 关键词: Computational; Methods; Medical; Image; Interpretation

Digital image data is being generated and collected at an astounding rate for numerous applications such as social media communication, defence and security, industrial manufacturing, agriculture and environment, science and biology, healthcare and medicine, etc. Today, static and dynamic images (video) constitute about 80% of all big data. This big ‘visual’ data hides within it extraordinary amount of information that is critical for providing new insights, building predictive models, guiding decision making, performing search and curation, etc. This is necessitating the design of new computational tools for image analysis that no longer is possible to carry out manually via visual inspection. The objective of the proposed research is to develop computer methods that address the key challenges towards automated, accurate, robust, and fast image analysis. Specifically, I will develop novel mathematical models and computational techniques for the following fundamental image interpretation tasks, which are necessary for harnessing visual information from raw image data: (i) image segmentation, to partition images into meaningful parts for subsequent quantification and decision making; (ii) image registration, for pair/group-wise image alignment enabling comparative studies and constructing probabilistic models of objects; and (iii) image classification, for discovering discriminatory visual patterns and features for assigning quantitative values or categorical class labels to visual data. More technically, I will focus on optimization-based formulations to solving the above three tasks. I will develop methods to construct the underlying objective functions by combining domain expert knowledge with machine learning techniques (from training databases). I will address the optimizabilty-fidelity tradeoff in these formulations by developing new representations for the unknowns (e.g. object shape geometry) that facilitate efficient optimization and inference. The complexity and variety of biomedical image data, the challenges facing their automated analysis, and the opportunities they provide for advancing healthcare, make them an ideal application domain for the proposed research. The image interpretation results will be invaluable for supporting diagnostics and therapeutics in many clinical applications. However, two applications will be emphasized given their societal and economic burden: oncology (e.g. organ and tumour delineation for radiation therapy) and neurology (e.g. discovering imaging biomarkers for neuro-development and neuro-degeneration).

数字图像数据正在以惊人的速度生成和收集，用于众多应用，如社交媒体通信，国防和安全，工业制造，农业和环境，科学和生物学，医疗保健和医学等。这种巨大的“视觉”数据隐藏着大量的信息，这些信息对于提供新的见解、构建预测模型、指导决策、执行搜索和策展等至关重要。 拟议研究的目标是开发计算机方法，以解决自动化，准确，鲁棒和快速图像分析的关键挑战。具体来说，我将开发新的数学模型和计算技术，用于以下基本图像解释任务，这是利用原始图像数据中的视觉信息所必需的：（i）图像分割，将图像划分为有意义的部分，用于随后的量化和决策; ㈡图像配准，进行成对/成组图像对齐，以便能够进行比较研究和建立物体的概率模型;以及（iii）图像分类，用于发现区别性的视觉模式和特征，以将定量值或分类类别标签分配给视觉数据。从技术上讲，我将专注于基于优化的公式来解决上述三个任务。我将开发方法，通过将领域专家知识与机器学习技术（来自训练数据库）相结合来构建底层目标函数。我将通过开发新的未知数（例如对象形状几何）表示来解决这些公式中的优化性-保真度权衡，以促进有效的优化和推理。 生物医学图像数据的复杂性和多样性，其自动化分析所面临的挑战，以及它们为推进医疗保健提供的机会，使其成为拟议研究的理想应用领域。图像解释结果对于支持许多临床应用中的诊断和治疗将是非常宝贵的。然而，考虑到其社会和经济负担，将强调两个应用：肿瘤学（例如放射治疗的器官和肿瘤描绘）和神经学（例如发现神经发育和神经变性的成像生物标志物）。