Biomedical Image Engineering of Macular Images

黄斑图像生物医学图像工程

• 批准号: 7122349
• 负责人: Roland THEODORE SMITH
• 金额: $ 41.89万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-15 至 2010-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7122349
• 关键词: bioimaging /biomedical imaging; biomedical automation; blood chemistry; clinical research; computer program /software; diagnosis design /evaluation; diagnosis quality /standard; digital imaging; disease /disorder classification; early diagnosis; eye disorder diagnosis; eye fundus photography; health economics; human genetic material tag; image enhancement; image processing; macular degeneration; macular drusen; mathematical model; molecular shape; pathologic process; questionnaires; retinography

DESCRIPTION (provided by applicant): This proposal will bring together a team with world-class expertise in ophthalmic disease, imaging and biomedical engineering to synthesize information from photographs and scanning laser ophthalmoscope (SLO) images to improve our understanding of age-related macular degeneration (AMD), the leading cause of blindness in the developed world. As we develop the technology to analyze these images, we will reach out to share and test our methods with internationally recognized centers for AMD research. We propose to develop accurate, cost-effective, automatic digital image analysis tools that are more efficient than the present manual methods. Cost considerations are particularly important in the increasingly strained health-care environment. For example, the cost for manual image analysis alone in an important NEI trial, the Age Related Eye Disease Study, was about $5.5M. Automated methods also lend themselves to streamlined sharing between institutions for further economies of scale and cost. The diagnosis and treatment of AMD are based on photography of the macula, hence the paramount importance of the information in these images. For example, the subretinal deposits known as drusen are the hallmark of this disease. Researchers have been trying for two decades, with limited results, to use digital techniques to quantify drusen. A major difficulty in identifying drusen is that the background in which they are embedded is inherently non-uniform. We have developed prototype automated mathematical models of the macula with sufficient power to overcome this obstacle and accurately identify drusen in the majority of images encountered in less than a minute. With sophisticated neural networks, level set deformable images, and other image engineering techniques from our colleagues in biomedical engineering that have proved effective for analysis of MRI images and mammograms, we propose to extend our capability to all macular images. One concept has been key to our efforts so far: leveling image background by a mathematical model for uniform object identification. This breakthrough may have wider significance: the ideas of computing such a model from partial background image data, and using it to remove background variability from the image, could be useful for identifying pathological structures in other types of medical images. The newer imaging technique of scanning laser ophthalmoscopy (SLO) can provide information about the biochemical basis of AMD, and in infrared mode, image subretinal structures. The SLO measures autofluorescence (AF), which reveals the accumulation of a potentially toxic substance called lipofuscin in the aging eye. The build-up of lipofuscin may be at least in part genetically determined. The gene known as ABCR definitely causes build-up in patients with juvenile macular degeneration and has been implicated in AMD. Linking this gene, or other candidate genes, with adult MD is the goal of our sister study, the Columbia Macular Genetics Study (CMGS). During the first three years we have been able to generate a clinical database of over 2,200 study subjects, and expect to reach our target of 3600 subjects in two more years, with macular photographs, SLO images and DNA samples from each subject. We propose to synthesize a wealth of information from the photographs and SLO images (AF and infrared) of the CMGS by combining our automated analyses of individual images with image registration of the three image types. For example, we have already analyzed AF images in registration with drusen photographs to provide evidence for a dramatic shift (from 75 percent to 20 percent) in the co-localization of drusen and hyperfluorescence from stage 3 (drusen) to stage 4 (drusen and geographic atrophy) AMD that may provide new insight on the natural history. In collaboration with King's College Hospital, London, we will pursue validation and understanding of these findings, which is now a major goal of this application. This is the type of work that could provide the linkage of specific DNA mutations to specific AMD image characteristics. Such knowledge could form the basis for early diagnosis of individuals at risk, who could then receive specific therapies based on specific molecular defects. These advances would extend profound health and social benefits to our aging population.

描述（由申请人提供）：该提案将汇集一支具有世界一流疾病，成像和生物医学工程专业知识的团队，以合成照片和扫描激光眼镜镜（SLO）图像的信息，以提高我们对年龄相关的黄斑变性（AMD）的理解，是发达世界中的盲目性引起的。当我们开发分析这些图像的技术时，我们将与国际公认的AMD研究中心共享和测试我们的方法。 我们建议开发与本手册方法更有效的准确，成本效益，自动数字图像分析工具。在日益紧张的医疗环境中，成本考虑因素尤为重要。例如，仅在重要的NEI试验中，仅手动图像分析的成本即与年龄相关的眼病研究，约为550万美元。 自动化方法还借助自己简化了机构之间的共享，以实现进一步的规模经济和成本经济。 AMD的诊断和处理是基于黄斑的摄影，因此这些图像中信息的重要性至关重要。例如，视网膜下沉积物称为drusen是这种疾病的标志。研究人员已经尝试了二十年，结果有限，以使用数字技术来量化drusen。识别drusen的主要困难是，它们嵌入的背景本质上是不均匀的。我们已经开发了大黄斑的原型自动数学模型，具有足够的力量来克服这一障碍，并准确地识别不到一分钟内遇到的大多数图像中的drusen。借助复杂的神经网络，水平设置可变形图像以及来自生物医学工程同事的其他图像工程技术已证明对MRI图像和乳房X线照片的分析有效，我们建议将我们的能力扩展到所有黄斑图像。到目前为止，一个概念是我们努力的关键：通过数学模型来平整图像背景，以识别统一对象。这种突破可能具有更大的意义：从部分背景图像数据中计算此类模型的想法，并使用它从图像中删除背景变异性，对于识别其他类型的医学图像中的病理结构可能很有用。 扫描激光眼镜镜（SLO）的较新成像技术可以提供有关AMD生化基础的信息，并以红外模式，图像下视网膜下结构。 SLO测量自动荧光（AF），揭示了衰老眼中称为脂肪霉素的潜在有毒物质的积累。脂肪霉素的积累至少可以部分在遗传上确定。被称为ABCR的基因无疑会导致少年黄斑变性患者的积累，并与A​​MD有关。将该基因或其他候选基因与成人MD联系起来是我们姐妹研究，即哥伦比亚黄斑遗传学研究（CMGS）的目标。在最初的三年中，我们能够生成一个超过2200名研究对象的临床数据库，并希望在两年内达到我们的3600名受试者的目标，其中包括黄斑照片，SLO图像和来自每个受试者的DNA样本。 我们建议通过将单个图像的自动分析与三种图像类型的图像注册相结合，从而从CMG的照片和SLO图像（AF和红外）中综合大量信息。例如，我们已经通过DRUSEN照片进行了对AF图像进行分析，以提供有关DRUSEN和从第3阶段（drusen）到第4阶段到第4阶段（drusen和Geography attrophy）AMD的DRUSEN和超荧光的共同定位的急剧转移（从75％到20％）的证据。在与伦敦的金学院医院合作的情况下，我们将对这些发现进行验证和理解，这现在是该申请的主要目标。这是一种可以提供特定DNA突变与特定AMD图像特征的连接的工作类型。这种知识可能是对处于危险中的个体的早期诊断的基础，然后他们可以根据特定的分子缺陷接受特定的疗法。这些进步将向我们的老龄化人口扩大深远的健康和社会益处。