Retinal image analysis software for neurodegenerative disease research

用于神经退行性疾病研究的视网膜图像分析软件

• 批准号: 9254634
• 负责人: Daniel Russakoff
• 金额: $ 22.3万
• 依托单位: VOXELERON, LLC
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-02-01 至 2018-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9254634
• 关键词: Affect; Age related macular degeneration; Agreement; Algorithms; Alzheimer' s Disease; American; Amyotrophic Lateral Sclerosis; Anatomy; Angiography; Atrophic; Belief; Biological Markers; Central Nervous System Diseases; Clinical; Clinical Research; Complex; Computer software; Cyst; Data; Data Analyses; Data Set; Databases; Development; Devices; Diabetic Retinopathy; Diagnosis; Disease; Disease Management; Disease Progression; Edema; Ensure; Epiretinal Membrane; Eye; Eye diseases; Ganglion Cell Layer; Generations; Glaucoma; Goals; Growth; Hand; Image; Image Analysis; Individual; Industry; Inflammation; Inflammatory; Inner Plexiform Layer; Liquid substance; Manuals; Manufacturer Name; Measurement; Measures; Methods; Modality; Monitor; Multiple Sclerosis; Nerve Degeneration; Neuraxis; Neurodegenerative Disorders; Nuclear; Observational Study; Ocular Pathology; Onset of illness; Ophthalmology; Optical Coherence Tomography; Outcome; Parkinson Disease; Pathologic; Pathology; Patients; Performance; Phase; Physicians; Primary Lateral Sclerosis; Process; Reporting; Research; Research Personnel; Resolution; Retina; Retinal; Retinal Diseases; Scanning; Scientist; Slice; System; Techniques; Technology; Testing; Thick; Thinness; Three-Dimensional Image; Time; Uveitis; Work; adaptive optics; aging population; biomarker development; brain health; clinical application; clinical biomarkers; clinical development; clinically relevant; cloud based; fovea centralis; ganglion cell; human imaging; image registration; improved; interest; longitudinal analysis; longitudinal dataset; macula; meetings; nervous system disorder; photoreceptor degeneration; software development; standard of care; theories; tool

Our goal is to develop and validate a device-independent software application for analysis of optical coherence tomography (OCT) images of the human retina. Our system will make quantitative measurements of retinal layer thicknesses at the macula in support of the generation of biomarkers for measuring onset and progression of ocular and neurodegenerative diseases, including age-related macular degeneration (AMD), diabetic retinopathy (DR), glaucoma, multiple sclerosis (MS), Alzheimer’s disease, Parkinson’s disease, and amytrophic lateral sclerosis (ALS). Retinal layer thicknesses indicate atrophy through thinning and increased fluid or inflammation through thickening. Accurate, device-independent segmentation of retinal layers together with longitudinal analysis of the layer thicknesses can return a number of quantitative biomarkers to correlate with disease onset and progression, and facilitate direct comparison across OCT devices to results from normal to estimate the degree of abnormalities. Specifically, we are aiming to: Aim 1 – Improve our segmentation of diseased eyes Orion (www.voxeleron.com/orion), our current research platform has been validated and used extensively on normal, non-pathologic eyes. Our structural analyses of retinal layers may be complicated, however, by ocular diseases or opacities prevalent in an aging population. Current software, ours included, can perform poorly in the case of disease, a situation we aim to ameliorate by improving our current segmentation algorithm in these cases and validating its performance on a large, hand-segmented dataset including AMD, DR, and glaucoma cases taken from at least 4 different device manufacturers’ OCT cameras. Aim 2 – Add longitudinal analysis to our segmentation software Clinically, static analysis of data has limited utility. We will add longitudinal analysis to our existing segmentation capabilities to measure the change of thicknesses over time. This new clinical workstation will be rigorously tested by determining agreement with expert-generated ground-truth.

我们的目标是开发和验证与设备无关的软件应用程序以分析光学连贯性 人类视网膜的断层扫描（OCT）图像。我们的系统将对视网膜进行定量测量 黄斑处的层厚度支持生成生物标志物，以测量和 眼和神经退行性疾病的进展，包括与年龄相关的黄斑变性（AMD）， 糖尿病性视网膜病（DR），青光眼，多发性硬化症（MS），阿尔茨海默氏病，帕金森氏病和 杏仁型侧索硬化症（ALS）。视网膜层厚度表明通过稀疏和增加 流体或通过增厚注射。永久性层的准确，独立于设备的分割 通过对层厚度的纵向分析，可以返回许多定量生物标志物以相关 随着疾病的发作和进展，并促进跨OCT设备的直接比较 正常估计异常程度。具体来说，我们的目标是： 目标1 - 改善我们患病的眼睛的细分 Orion（www.voxeleron.com/orion），我们当前的研究平台已被验证和广泛使用 正常的，非病理学的眼睛。但是，我们对视网膜层的结构分析可能会复杂 在老龄化人群中普遍存在的疾病或院。当前的软件（包括我们的）在 疾病的情况，我们旨在通过改善当前分割算法来改善这些情况 案例并验证其在包括AMD，DR和青光眼在内的大型手动分段数据集中的性能 从至少4个不同的设备制造商的OCT摄像机中取出的案例。 目标2 - 在我们的分割软件中添加纵向分析 临床上，数据的静态分析有限。我们将为我们现有的纵向分析添加 分割能力，以测量随时间变化的变化。这个新的临床工作站将 通过确定与专家生成的地面真相的一致性，可以严格测试。