Deep graphical models and methods for multi-modal biomedical image processing, analysis, and interpretation

用于多模态生物医学图像处理、分析和解释的深度图形模型和方法

• 批准号: RGPIN-2018-03966
• 负责人: Wong, Alexander
• 金额: $ 3.35万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=711704
• 关键词: Deep; graphical; models; methods; multi

Multi-modal imaging, where a multitude of imaging techniques are conducted in a single examination, has become an integral and crucial part of the modern healthcare system, as well as a powerful tool leveraged by research scientists to deepen the the understanding of diseases. However, due to the immense quantity as well as complexities of the acquired multi-modal imaging data, along with trade-offs between image quality and image acquisition times, there are significant challenges for clinicians and research scientists to interpret, understand, and analyse the acquired data in a semantically meaningful and efficient fashion. As such, novel methods for computer-aided processing, analysis, and interpretation of this wealth of multi-parametric imaging data can lead to significant improvements in not only disease screening and diagnosis, disease treatment planning and management, but also disease understanding. The main goal of the proposed research program is to develop novel computational models and intelligent algorithms for multi-modal biomedical image processing, analysis, and interpretation. Four main objectives will be investigated and explored: 1) deep graphical models and methods for improving the reconstruction and enhancement of acquired multi-modal imaging data, 2) novel deep graphical models for better characterizing the complex information captured in the acquired multi-modal imaging data, 3) deep model-driven image analysis for efficient and accurate extraction of quantitative information from a wealth of multi-modal imaging data in an explainable manner, and 4) deep model-driven artificial intelligence methods for efficient, accurate, and explainable computer-aided decision-making. The scientific and engineering results of the proposed research program will have a significant impact on the health and well-being of Canadians by improving disease screening and diagnosis, disease treatment planning and management, and improving disease understanding by providing new insights into the traits and mechanisms of disease through multi-modal imaging. The scientific knowledge and technologies developed during the proposed research program will be transferred into industry through active collaborations with companies such as Christie Medical, Agfa Healthcare, Hill-Rom Inc., and Elucid Labs. Furthermore, HQP will continue to be trained in image processing and analysis, computer vision, and artificial intelligence within a multi-disciplinary environment, putting them in a strong position for leadership roles in industry as well as in academia.

多模式成像，即在一次检查中进行多种成像技术，已经成为现代医疗体系中不可或缺的关键部分，也是研究科学家用来加深对疾病理解的强大工具。然而，由于采集的多模式成像数据的数量和复杂性，以及图像质量和图像采集时间之间的权衡，临床医生和研究科学家在以语义有意义和有效的方式解释、理解和分析采集的数据方面面临着巨大的挑战。因此，对这些丰富的多参数成像数据进行计算机辅助处理、分析和解释的新方法不仅可以显著改进疾病筛查和诊断、疾病治疗计划和管理，而且还可以显著提高对疾病的理解。 提出的研究计划的主要目标是为多模式生物医学图像处理、分析和解释开发新的计算模型和智能算法。将研究和探索四个主要目标：1)用于改进所获取的多模式成像数据的重建和增强的深层图形模型和方法，2)用于更好地表征从所获取的多模式成像数据中捕获的复杂信息的新型深层图形模型，3)用于以可解释的方式从丰富的多模式成像数据中高效和准确地提取定量信息的深层模型驱动的图像分析，以及4)用于高效、准确和可解释的计算机辅助决策的深层模型驱动的人工智能方法。 拟议研究计划的科学和工程成果将改善疾病筛查和诊断、疾病治疗计划和管理，并通过多模式成像提供对疾病特征和机制的新见解，从而对加拿大人的健康和福祉产生重大影响。在拟议的研究计划期间开发的科学知识和技术将通过与Christie Medical、Agfa Healthcare、Hill-Rom Inc.和Elucid Labs等公司的积极合作而转化为工业。此外，HQP将继续在多学科环境中接受图像处理和分析、计算机视觉和人工智能方面的培训，使他们在行业和学术界处于领导地位。