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）：该提案将汇集在眼科疾病、成像和生物医学工程领域拥有世界一流专业知识的团队，以综合照片和扫描激光检眼镜（SLO）图像中的信息，以提高我们对年龄相关性黄斑变性的了解(AMD)，是发达国家失明的主要原因。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 研究中心分享和测试我们的方法。 我们建议开发准确、经济高效的自动数字图像分析工具，比现有的手动方法更有效。在日益紧张的医疗保健环境中，成本考虑尤为重要。例如，在一项重要的 NEI 试验“年龄相关眼病研究”中，仅手动图像分析的成本就约为 550 万美元。 自动化方法还有助于简化机构之间的共享，以进一步实现规模经济和成本经济。 AMD 的诊断和治疗基于黄斑摄影，因此这些图像中的信息至关重要。例如，称为玻璃膜疣的视网膜下沉积物是这种疾病的标志。二十年来，研究人员一直在尝试使用数字技术来量化玻璃疣，但成果有限。识别玻璃疣的一个主要困难是它们嵌入的背景本质上是不均匀的。我们开发了黄斑自动化数学模型原型，具有足够的能力来克服这一障碍，并在不到一分钟的时间内准确识别大多数图像中的玻璃疣。凭借复杂的神经网络、水平集可变形图像以及生物医学工程同事提供的其他图像工程技术，这些技术已被证明可有效分析 MRI 图像和乳房 X 光照片，我们建议将我们的能力扩展到所有黄斑图像。到目前为止，一个概念一直是我们努力的关键：通过统一对象识别的数学模型来调平图像背景。这一突破可能具有更广泛的意义：从部分背景图像数据计算此类模型并使用它来消除图像中的背景变异性的想法可能有助于识别其他类型的医学图像中的病理结构。 更新的扫描激光检眼镜 (SLO) 成像技术可以提供有关 AMD 生化基础的信息，并在红外模式下对视网膜下结构进行成像。 SLO 测量自发荧光 (AF)，它揭示了一种称为脂褐质的潜在有毒物质在老化眼睛中的积累。脂褐素的积累可能至少部分是由遗传决定的。 ABCR 基因肯定会导致青少年黄斑变性患者的病情加重，并且与 AMD 相关。我们的姊妹研究哥伦比亚黄斑遗传学研究 (CMGS) 的目标是将该基因或其他候选基因与成人 MD 联系起来。在头三年里，我们已经能够生成超过 2,200 名研究对象的临床数据库，并预计在两年内达到 3600 名对象的目标，其中包括每个对象的黄斑照片、SLO 图像和 DNA 样本。 我们建议通过将对单个图像的自动分析与三种图像类型的图像配准相结合，从 CMGS 的照片和 SLO 图像（AF 和红外）中合成大量信息。例如，我们已经分析了与玻璃膜疣照片配准的 AF 图像，为玻璃膜疣和强荧光的共定位从第 3 阶段（玻璃膜疣）到第 4 阶段（玻璃膜疣和地理萎缩）AMD可能会为自然历史提供新的见解。我们将与伦敦国王学院医院合作，对这些研究结果进行验证和理解，这是目前该应用程序的主要目标。此类工作可以提供特定 DNA 突变与特定 AMD 图像特征的联系。这些知识可以为高危个体的早期诊断奠定基础，然后这些人可以根据特定的分子缺陷接受特定的治疗。这些进步将为我们的老龄化人口带来深远的健康和社会效益。