Fully-Automated Lesion Characterization in Ultrawide-Field Retinal Images

超宽视场视网膜图像中的全自动病变表征

基本信息

批准号：
10082348
负责人：
Sandeep Bhat
金额：
$ 100万
依托单位：
EYENUK, INC.
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-06-01 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10082348
关键词：
Agreement Algorithms Applications Grants Biological Blindness Cataract Characteristics Classification Clinical Clinical Data Color Competence Computer software Coupled Data Set Detection Development Diabetes Mellitus Diabetic Retinopathy Diagnostic Disease Early Diagnosis Ensure Exposure to Eye Eye diseases Eyelash Eyelid structure Goals Gold Image Image Analysis Incidence Institutes Internet Lasers Lesion Light Localized Lesion Manuals Measures Modality Morphologic artifacts Online Systems Ophthalmoscopy Patient Triage Patients Penetration Peripheral Phase Research Retina Retinal Diseases Risk Scanning Scheme Scientist Screening procedure Severities Small Business Innovation Research Grant Software Engineering Speed Surveys System Testing Time Training Validation Vision Work automated analysis base cloud based deep learning design diabetic diabetic patient digital drug discovery experience fundus imaging high risk image processing imaging modality interest proliferative diabetic retinopathy retinal imaging screening screening program success tool usability

项目摘要

Abstract In this grant application we propose to develop, EyeReadUWF, a fully automated tool for lesion characterization in ultra-widefield scanning laser ophthalmoscopy (UWF SLO) images. In recent times non mydriatic UWF SLO imaging has been shown to be a promising alternative to conventional digital color fundus imaging for grading of diabetic eye diseases, with advantages including 130°-200° field-of-view showing more than 80% of the retina in a single image, no need for multiple fields, multiple flashes, or refocusing between field acquisitions, ability to penetrate media opacities like cataract, and lower rate of ungradable images. UWF SLO images are particularly suitable for detecting predominantly peripheral lesions (PPLs), which have been associated with higher risk of diabetic retinopathy (DR) progression. Accurate quantification of presence and extent of PPLs can only be done by a robust automated tool that is specifically designed for the pseudo-colored images of UWF SLO modality. EyeReadUWF will automatically characterize lesions in pseudo colored UWF images while handling possible artifacts from eyelashes/eyelids and determine the lesion predominance in peripheral and central regions of UWF image. The ability to accurately quantify the presence and extent of predominantly peripheral lesions in UWF SLO images can enable clinicians to develop a more precise DR scoring scheme. This would help identify patients with higher risk of DR progression and onset of PDR, have a positive impact on diabetic patient management, and aid drug discovery research.

抽象的在此拨款申请中，我们建议开发 EyeReadUWF，这是一种全自动工具，用于超宽场扫描激光检眼镜 (UWF SLO) 图像中的病变特征。最近，免散瞳 UWF SLO 成像已被证明是一种有前途的替代方案数字化常规彩色眼底成像对糖尿病眼病分级具有优势包括 130°-200° 视野，在单个图像中显示超过 80% 的视网膜，无需要多个视场、多次闪光或在视场采集之间重新对焦、能够穿透像白内障一样的介质不透明度，并降低不可分级图像的比率。特别适合检测主要外围区域（PPL），这些区域已被与糖尿病视网膜病变 (DR) 进展的较高风险相关。 PPL 的存在和范围只能通过专门针对的强大自动化工具来完成专为 UWF SLO 模态的伪彩色图像而设计。 EyeReadUWF 将自动表征伪彩色 UWF 图像中的病变，同时处理睫毛/眼睑可能出现的伪影，并确定病变的优势 UWF 图像的外围和中心区域能够准确量化其存在。 UWF SLO 图像中主要是外周病变的范围和范围可以优于制定更精确的 DR 评分方案这将有助于识别具有较高风险的患者。 DR 进展和 PDR 发生对糖尿病患者管理产生积极影响，并且帮助药物发现研究。