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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=636966
• 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视图。然而，这些机器本质上是复杂的“相机”或“眼睛”。尽管它们能生成漂亮的图片，但它们不能量化或“理解”这些图片。临床医生通常在视觉上和定性上检查这些图像，因为临床上还没有从这些图像中进行准确测量的工具。开发“眼睛”背后的“大脑”，或图像背后的智能算法，可以将原始成像数据转换为测量数据，用于检测疾病的发病，自信地诊断疾病，或定量监控疾病进展，这是我研究计划的动机和长期目标。焦点：我的研究计划专注于开发一个计算解剖学框架，以挖掘与阿尔茨海默病(AD)等脑部疾病相关的生物标记物的3D脑磁共振(MR)图像。第二个重点是开发一种新的计算解剖学框架，用于解释视网膜的3D光学相干断层扫描(OCT)图像，以寻找与老年性黄斑变性(AMD)或青光眼等眼病相关的生物标志物。由于视网膜是大脑的神经感觉延伸，来自视网膜图像的生物标记物不仅对改善视力护理有价值，而且在未来，还可能作为大脑生物标记物的替代品，并潜在地帮助诊断或监测大脑疾病的进展。挑战：每张3D图像通常有数百万个样本和测量数据。感兴趣的信号，即疾病引起的变化，通常局限于构成图像的体素的子集(例如，记忆电路驻留的海马体，或视神经纤维离开眼睛的视神经头)。大型三维体图像中感兴趣区域的自动识别(分割)是一项具有挑战性的重要任务，因为所有下游测量都依赖于这第一个任务。一旦分割，从感兴趣区域(ROI)获得的每个形态测量向量就是高维空间中的一个点。第二个关键挑战是区分信号(由于疾病或状况导致的ROI的变化)和“扩散”，即由于人群中ROI的正常变化而产生的变异性。研究目标：提高自动分割、配准和测量的准确性，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新的定量生物标记物。意义：大脑和眼睛疾病是65岁以上加拿大人残疾的主要原因。它们导致个人及其家庭的生活质量低下，并每年给加拿大造成数十亿美元的损失。建议的框架有可能填补定量生物医学图像解释技术中尚未满足的主要需求，帮助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，从而允许以更低的成本提供更高质量的医疗保健。进展：过去的探索资助周期对我的实验室来说是非常有成效的，有25份高影响力的期刊出版物和17份同行评议的会议出版物。我获得了享有盛誉的迈克尔·史密斯健康研究基金会学者奖和不列颠哥伦比亚省专业工程师和地球科学家协会的功勋成就奖，以表彰我在设计临床适用的计算工具方面的贡献。在这些优势的基础上，我创建了一个令人兴奋的研究计划，以期在定量生物医学图像解释方面有新的发现，在过去的一个周期中，我指导了36名实习生。