Brains behind the eyes: Interpreting Medical Images

眼睛后面的大脑：解读医学图像

• 批准号: RGPIN-2014-03953
• 负责人: Beg, MirzaFaisal
• 金额: $ 3.72万
• 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=610744
• 关键词: Brains; behind; eyes; Interpreting; Medical

Motivation: Modern medical imagers generate detailed images providing an unprecedented view of the internal anatomy. However, these machines are essentially sophisticated ‘cameras’ or ‘eyes’. Though they generate pretty pictures, they cannot quantify or ‘understand’ these images. The clinicians typically examine these images visually and qualitatively since tools for making accurate measurements from these images are not yet available in the clinic. Developing the ‘brains’ behind the ‘eyes’, or the intelligent algorithms behind the images 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 motivation and the long term goal of my research program. Focus: My research program focuses on developing a computational anatomy framework to mine the 3D brain magnetic resonance (MR) images for biomarkers related to brain diseases such as Alzheimer’s disease (AD). A second focus is to develop a novel computational anatomy framework for interpreting 3D optical coherence tomography (OCT) images of the retina for biomarkers related to eye diseases such as age-related macular degeneration (AMD) or glaucoma. Since the retina is a neurosensory extension of the brain, biomarkers from retinal images are not only valuable for improving vision care, but in the future, may also serve as surrogates for brain biomarkers and potentially help in diagnosing or monitoring the progression of brain diseases. Challenges: Each 3D image typically has millions of samples and measurements. The signal of interest i.e. the changes due to disease, are typically localized in a subset of the voxels that make an image (such as the hippocampus, where memory circuits reside, or the optic nerve head, where optic nerve fibers exit the eye). Automated identification of regions of interest (segmentation) in large 3D volumetric images is a challenging and important task for all downstream measurements depend on this first task. Once segmented, each vector of morphometric measurements taken from a region of interest (ROI) is a point in a high-dimensional space. Distinguishing the signal (the changes in a ROI due to a disease or condition) from the ‘spread’, or variability due to normal variation in the ROI in the population, is a second key challenge. Research goals: are to increase accuracy in automated segmentation, registration and measurements, design better separable features and classifiers leading to the discovery of novel quantitative biomarkers for disease diagnosis and progression. Significance: Brain and eye diseases are the primary cause of disability among Canadians over the age of 65. They lead to poor quality of life for the individual and their family, and cost Canada billions of dollars each year. The proposed frameworks have the potential fill a major, unmet need in quantitative biomedical image interpretation technologies, help better utilize information present in imaging data, save valuable clinician time, and thus allow better quality health care to be delivered at lower cost. Progress: The past Discovery grant cycle has been highly productive for my lab, with 25 high-impact journal publications and 17 peer-reviewed conference publications. I was awarded the prestigious Michael Smith Foundation for Health Research Scholar award and the Association of Professional Engineers & Geoscientists of BC’s Meritorious Achievement award for my contributions to designing clinically applicable computational tools. Building on these strengths, I have created an exciting research program towards new discoveries in quantitative biomedical image interpretation in which I supervised 36 trainees in the past cycle.

动机：现代医学成像仪生成详细的图像，提供前所未有的内部解剖结构视图，但是，这些机器本质上是复杂的“相机”或“眼睛”，尽管它们可以生成漂亮的图像，但它们通常无法量化或“理解”这些图像。由于临床上尚未开发出“眼睛”背后的“大脑”，或者可以将原始成像数据转换为可测量的图像背后的智能算法，因此可以从视觉上和定性上对这些图像进行精确测量。习惯于检测疾病的发作、自信地诊断疾病或定量监测疾病进展是我研究项目的动机和长期目标。 重点：我的研究项目重点是开发一个计算解剖框架来挖掘 3D 脑磁共振 (MR) 图像，以查找与阿尔茨海默氏病 (AD) 等脑部疾病相关的生物标志物。视网膜 3D 光学相干断层扫描 (OCT) 图像，用于检测与年龄相关性黄斑变性 (AMD) 或青光眼等眼部疾病相关的生物标志物。来自视网膜图像的生物标志物不仅对于改善视力保健有价值，而且在未来还可以作为大脑生物标志物的替代品，并可能有助于诊断或监测脑部疾病的进展。 挑战：每个 3D 图像通常都有数百万个样本和测量结果，感兴趣的信号（即由于疾病引起的变化）通常位于生成图像的体素子集中（例如存储电路所在的海马体或大脑中的神经元）。自动识别大型 3D 体积图像中的感兴趣区域（分割）是一项具有挑战性且重要的任务，因为所有下游测量都依赖于这第一个任务。分割后，从感兴趣区域 (ROI) 获取的每个形态测量向量都是高维空间中的一个点，可将信号（由于疾病或状况引起的 ROI 变化）与“传播”区分开来。第二个关键挑战是由于总体投资回报率的正常变化而导致的变异性。 研究目标：提高自动分割、注册和测量的准确性，设计更好的可分离特征和分类器，从而发现用于疾病诊断和进展的定量新型生物标志物。 意义：脑部和眼部疾病是 65 岁以上加拿大人残疾的主要原因。这些疾病导致个人及其家庭的生活质量下降，每年给加拿大造成数十亿美元的损失。拟议的框架具有潜在的填补作用。生物医学图像解释技术的一个主要的、未满足的需求，有助于更好地利用成像数据中存在的信息，节省宝贵的临床医生时间，从而定量地允许以更低的成本提供更高质量的医疗保健。 进展：过去的 Discovery 资助周期对我的实验室来说非常富有成效，发表了 25 篇高影响力的期刊出版物和 17 篇同行评审的会议出版物，我被授予著名的迈克尔·史密斯健康研究基金会奖和专业工程师与协会奖。 BC 省地球科学家杰出成就奖表彰我在设计临床适用的计算工具方面的贡献。基于这些优势，我创建了一个令人兴奋的研究项目，以期在定量生物医学图像解释方面取得新发现，在该项目中，我指导了 36 名学员。过去的周期。