Brains behind the eyes: Interpreting Medical Images

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”。虽然它们生成了漂亮的图像，但它们无法量化或“理解”这些图像。临床医生通常在视觉上和定性地检查这些图像，因为用于从这些图像进行精确测量的工具在临床中还不可用。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可用于检测疾病发作，自信地诊断疾病或定量监测疾病进展的测量值，这是我研究计划的动机和长期目标。聚焦：我的研究计划侧重于开发一个计算解剖学框架，以挖掘3D脑磁共振（MR）图像中与阿尔茨海默病（AD）等脑部疾病相关的生物标志物。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，以获得与年龄相关性黄斑变性（AMD）或青光眼等眼科疾病相关的生物标志物。由于视网膜是大脑的神经感觉延伸，因此视网膜图像中的生物标志物不仅对改善视力保健有价值，而且在未来还可能作为大脑生物标志物的替代物，并可能有助于诊断或监测脑部疾病的进展。挑战：每个3D图像通常具有数百万个样本和测量值。感兴趣的信号，即由于疾病引起的变化，通常定位在形成图像的体素的子集中（诸如海马体，其中存储器电路驻留，或视神经头，其中视神经纤维离开眼睛）。在大型3D体积图像中自动识别感兴趣区域（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项任务。一旦被分割，从感兴趣区域（ROI）获取的形态测量的每个向量是高维空间中的点。区分信号（由于疾病或病症引起的ROI变化）与“扩散”或由于群体中ROI的正常变化引起的变异性是第二个关键挑战。研究目标：是为了提高自动分割，注册和测量的准确性，设计更好的可分离的功能和分类器，导致发现新的定量生物标志物的疾病诊断和progress.Significance：脑和眼睛疾病是残疾的主要原因在加拿大人超过65岁。它们导致个人及其家庭的生活质量低下，每年使加拿大损失数十亿美元。拟议的框架有可能填补一个主要的，未满足的需求，在定量生物医学图像解释技术，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的时间，从而使更好的质量以更低的成本提供医疗保健。进度：在过去的探索资助周期中，我的实验室取得了很高的成果，发表了25篇具有高影响力的期刊论文和17篇同行评议的会议论文。我被授予著名的迈克尔·史密斯基金会健康研究学者奖和专业工程师协会&卑诗省地球科学家的功勋成就奖，以表彰我对设计临床适用的计算工具的贡献。在这些优势的基础上，我创建了一个令人兴奋的研究计划，旨在实现定量生物医学图像解释的新发现，在过去的一个周期中，我监督了36名学员。

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.