Novel optimization strategies for medical image analysis

医学图像分析的新颖优化策略

• 批准号: 298324-2010
• 负责人: Hamarneh, Ghassan
• 金额: $ 2.48万
• 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: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=525976
• 关键词: Novel; optimization; strategies; medical; image

Anatomical and functional medical imaging modalities, e.g. MRI and PET, are allowing physicians to peer inside the human body and observe a wealth of data crucial for understanding, diagnosing and treating diseases. The volume of medical data acquired is growing rapidly. The dimensionality of images has increased from 2D scalar images to dynamic 3D multi-valued fields. This is resulting in image data that cannot be effectively processed with traditional visual inspection. Therefore, automated computational tools for medical image analysis (MIA) are becoming indispensable in modern healthcare systems. The three most important and ubiquitous MIA tasks are image segmentation, registration, and shape analysis, which constitute the crux of image interpretation and quantification tasks. Segmentation is the process of identifying regions of interest in an image (e.g. to measure wall thickness of the myocardium), whereas registration is the process of finding meaningful correspondence between images (e.g. to compare across subjects or time). Shape analysis captures geometric and topological properties and reveals crucial information about disease stages, treatment progress, or growth rates. Despite numerous advances in these areas in the past few decades, accurate and automatic MIA algorithms continue to defy solution. The vast majority of these algorithms rely on solving optimization problems. However, very little work has been devoted to evaluating the appropriateness of the objective functions being optimized or to integrating high-level domain knowledge into the process. This proposal will focus on studying formal approaches for evaluating objective functions and designing them from the outset using rigorous mathematical and computational techniques. This research will also complement low-level optimization techniques with high-level, knowledge-driven MIA strategies using a novel artificial life framework. The goal is to ensure higher accuracy of automated MIA algorithms, in order to advance computer-aided diagnosis, computer-assisted interventions, and the many other aspects of healthcare that rely on medical imaging.

解剖和功能医学成像方式，如MRI和PET，使医生能够窥视人体内部，观察对理解、诊断和治疗疾病至关重要的大量数据。获取的医疗数据量正在快速增长。图像的维度已经从2D标量图像增加到动态3D多值场。这导致图像数据不能用传统的目视检查有效地处理。因此，用于医学图像分析的自动化计算工具(MIA)在现代医疗系统中变得不可或缺。图像分割、配准和形状分析是MIA中最重要和最普遍的三个任务，它们构成了图像解释和量化任务的关键。分割是识别图像中感兴趣区域的过程(例如，测量心肌壁厚度)，而配准是找到图像之间有意义的对应关系的过程(例如，跨受试者或时间进行比较)。形状分析捕捉几何和拓扑属性，并揭示有关疾病分期、治疗进度或增长率的关键信息。尽管在过去的几十年里在这些领域取得了许多进展，但准确和自动的MIA算法仍然难以解决。这些算法中的绝大多数依赖于解决优化问题。然而，很少有人致力于评估正在优化的目标函数的适当性或将高级领域知识整合到这一过程中。该提案将侧重于研究评价目标函数的正式方法，并从一开始就使用严格的数学和计算技术进行设计。这项研究还将使用一种新的人工生命框架，用高级的、知识驱动的MIA策略来补充低级别的优化技术。其目标是确保自动MIA算法的更高精度，以便推进计算机辅助诊断、计算机辅助干预以及依赖医学成像的医疗保健的许多其他方面。