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: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=548299
• 关键词: Novel; optimization; strategies; medical; image; 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.

解剖学和功能性医学成像方式，例如MRI和PET允许医生在人体内部凝视，并观察到有关理解，诊断和治疗疾病至关重要的大量数据。获得的医疗数据量正在迅速增长。图像的维度已从2D标量图像增加到动态3D多值字段。这导致图像数据无法通过传统的视觉检查有效处理。因此，在现代医疗保健系统中，自动化的医学图像分析计算工具（MIA）变得必不可少。最重要且无处不在的MIA任务是图像分割，注册和形状分析，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的区域的过程（例如测量心肌的壁厚），而注册是在图像之间找到有意义的对应关系的过程（例如，比较跨受试者或时间）。形状分析捕获了几何和拓扑特性，并揭示了有关疾病阶段，治疗进度或生长速率的关键信息。尽管在过去几十年中，这些领域的进步都有许多进步，但准确和自动的MIA算法继续反抗解决方案。这些算法中的绝大多数都依赖于解决优化问题。但是，很少有工作专门用于评估被优化的目标函数的适当性或将高级领域知识整合到过程中。该建议将着重于研究正式方法，以评估目标功能并使用严格的数学和计算技术从一开始就设计它们。这项研究还将使用一种新型的人工生命框架与高级，知识驱动的MIA策略相辅相成。目的是确保自动化MIA算法的更高准确性，以推进计算机辅助诊断，计算机辅助干预措施以及依赖医学成像的医疗保健的许多其他方面。