Brains behind the eyes: Interpreting Medical Images

眼睛后面的大脑：解读医学图像

• 批准号: RGPIN-2019-06939
• 负责人: Beg, MirzaFaisal
• 金额: $ 4.01万
• 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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752978
• 关键词: Brains; behind; eyes; Interpreting; Medical

Modern medical imagers generate detailed images but they cannot quantify or `understand' these images. Computational models that can be used to interpret these medical images are needed to understand "normal" shape and function and identify deviations that may signal the onset of disease and to quantify the pace and temporal individual variability. This is the emerging field of "computational disease vision," with close parallels to computer vision, where intelligent algorithms are designed specifically for analyzing and recognizing the signals present in medical images. Developing the `brains' behind the `eyes', or the intelligent computer vision algorithms that can convert raw imaging data into measurements that can be used to detect the onset of disease, diagnose a disease with confidence, or to quantitatively monitor disease progression is the long-term goal of my research program.  The hypothesis is that machine learning models can extract and understand deeper relationships present in medical imaging data than are possible with human visual analysis. Hence, such computational vision algorithms are undoubtedly the future for clinical image interpretation. We propose two specific short-term goals towards the overarching long term quest for disease recognition from medical images. These are: (1) design of spatio-temporal multi-scale, multi-modal structured representations of normative and disease signatures, and (2) design of conventional, mixed- and deep-models for developing novel classifiers for disease recognition. Building holistic models for understanding human anatomy, shape and function, and thereby, models for recognition and quantification of disease present significant challenges. There is inherent variability across normative state in the population, and hence the signal of interest can be subtle and weak as compared to normal variability. The signals that mark the onset of disease exist and multiple scales, and are often described in relative terms of a configuration change, and hence require a semantic representation that can capture multiple levels of interaction across scales and locations within the anatomy. Often, a single modality may only capture part of the changes, for example, in the retina, the changes in retina layer geometry may be weaker but in addition to changes in the retina vasculature, provide a stronger discriminant signal to recognise the quantify disease. We propose to develop novel extensions to conventional shape models, deep-structured models that act on raw medical images directly, as well as mixed models combining the best of both conventional and deep-structured models for automated disease recognition from medical images in this proposal.

现代医学成像仪生成详细的图像，但它们不能量化或“理解”这些图像。需要可用于解释这些医学图像的计算模型来理解“正常”形状和功能，并识别可能预示疾病发作的偏差，并量化起搏和时间个体变异性。这是新兴的“计算疾病视觉”领域，与计算机视觉密切相关，其中智能算法专门用于分析和识别医学图像中存在的信号。开发“眼睛”后面的“大脑”，或智能计算机视觉算法，可以将原始成像数据转换为可用于检测疾病发作的测量值，有信心地诊断疾病，或定量监测疾病进展是长期的-我的研究计划的长期目标。假设是机器学习模型可以提取和理解医学成像数据中存在的更深层次的关系比人类的视觉分析更准确。因此，这种计算视觉算法无疑是临床图像解释的未来。我们提出了两个具体的短期目标，对总体的长期追求的疾病识别从医学图像。这些是：（1）规范和疾病签名的时空多尺度、多模态结构化表示的设计，以及（2）用于开发用于疾病识别的新型分类器的常规、混合和深度模型的设计。 构建用于理解人体解剖结构、形状和功能的整体模型，从而构建用于识别和量化疾病的模型，这是一个重大挑战。在群体的正常状态下存在固有的变异性，因此与正常变异性相比，感兴趣的信号可能是微妙和微弱的。标记疾病发作的信号存在多个尺度，并且通常以配置变化的相对术语来描述，因此需要可以捕获跨解剖结构内的尺度和位置的多个水平的相互作用的语义表示。通常，单个模态可能仅捕获部分变化，例如，在视网膜中，视网膜层几何结构的变化可能较弱，但除了视网膜脉管系统的变化之外，还提供更强的判别信号以识别量化疾病。我们建议开发传统形状模型的新扩展，直接作用于原始医学图像的深度结构化模型，以及将传统和深度结构化模型的最佳组合用于从医学图像中自动识别疾病的混合模型。