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

基本信息

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
项目类别：
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&apos 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

项目摘要

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独立于设备的软件应用程序，用于分析光学相干性我们的系统将对人类视网膜的断层扫描 (OCT) 图像进行定量测量。黄斑处的层厚度支持生成用于测量发病和发生的生物标记物眼部和神经退行性疾病的进展，包括年龄相关性黄斑变性（AMD），糖尿病视网膜病变 (DR)、青光眼、多发性硬化症 (MS)、阿尔茨海默病、帕金森病和肌萎缩侧索硬化症（ALS）表明视网膜层厚度因变薄和增加而萎缩。通过增厚准确、独立于设备的视网膜层分割来消除液体或炎症。通过层厚度的纵向分析可以返回许多定量生物标志物以进行关联疾病的发作和进展，并促进 OCT 设备与结果的直接比较具体来说，我们的目标是：目标 1 – 改善患病眼睛的分割 Orion (www.voxeleron.com/orion)，我们当前的研究平台已经过验证并广泛用于然而，对于正常的、非病理性的眼睛，我们对视网膜层的结构分析可能会很复杂。当前的软件（包括我们的软件）在人口老龄化中普遍存在的疾病或混浊情况可能表现不佳。就疾病而言，我们旨在通过改进当前的分割算法来改善这种情况案例并在大型手动分割数据集（包括 AMD、DR 和青光眼）上验证其性能案例取自至少 4 个不同设备制造商的 OCT 相机。目标 2 – 在我们的细分软件中添加纵向分析在临床上，数据的静态分析的效用有限，我们将在现有的基础上添加纵向分析。这种新的临床工作站将具有测量厚度随时间变化的分割功能。通过确定与专家生成的地面事实的一致性来进行严格的测试。