Exploring Clinically-relevant Image Retrieval for Diabetic Retinopathy Diagnosis

描述(申请人提供)：所有糖尿病患者都有发生糖尿病视网膜病变的风险，这是一种威胁视力的并发症。尽管多年来糖尿病治疗取得了进展，但糖尿病视网膜病变仍然是一种潜在的破坏性并发症。及早发现，及时干预或治疗，可以降低糖尿病视网膜病变致盲的发生率。近年来，基于数字视网膜成像的诊断已成为传统面对面评估的替代方案。现有的研究表明，自动分析糖尿病视网膜病变的数字图像具有潜在的好处。然而，目前没有一个基于计算机的系统可以达到与人类专家相同的性能水平。这项应用为开发用于改进糖尿病视网膜病变诊断的计算机辅助系统提供了新的视角，通过探索针对给定的新图像从具有先前诊断信息的存档数据库中检索与临床相关的图像的新的计算方法。如果图像包含相同类型的病变，但严重程度相似，则认为它们具有临床相关性。基于内容的视网膜图像搜索/检索的研究和开发仍处于起步阶段，仅取得了有限的成功，这在很大程度上是由于将专家知识显式编码到计算算法中的挑战。为了应对这一挑战，该研究项目采取了一种截然不同的方法，采用了机器学习的方法，其中使用标记的图像集来训练计算机算法来分析其他新图像，重点是训练临床相关性的相似性，而不是图像特征。培训在一定程度上是由于调查人员对基于计算机的病变模拟的现有研究。这项研究的一个具体目标是建立一个基于内容的图像检索系统，该系统可以为临床医生提供与被诊断图像临床相关的档案图像的即时参考。这是一种创新的方式，利用隐藏在先前诊断的糖尿病视网膜病变数字图像库中的大量专家知识，为临床医生提高诊断性能提供帮助。另一个具体目标是建立一个糖尿病视网膜病变的图像信息管理系统，支持检索系统在现实的临床环境中的部署。除了检索系统，这项研究的直接结果还包括糖尿病视网膜病变图像的自动评估算法，与现有方法相比，这些算法的性能可能会有所改善。特别是，拟议工作的设计允许根据医生的特定需求对结果系统进行不同的配置。 公共卫生相关性：该项目开发了一个基于计算机的系统，用于改进糖尿病视网膜病变的诊断，糖尿病视网膜病变是糖尿病患者的一种威胁视力的并发症。早期发现和及时干预或治疗可以减少失明的发生率，而基于计算机的系统不仅可以提高诊断或筛查糖尿病视网膜病变患者的速度和准确性。

DESCRIPTION (provided by applicant): All people with diabetes have the risk of developing diabetic retinopathy, a vision-threatening complication. Despite advances in diabetes care over the years, diabetic retinopathy remains a potentially devastating complication. Early detection and timely intervention or treatment can reduce the incidence of blindness due to diabetic retinopathy. Recent years, diagnosis based on digital retinal imaging has become an alternative to traditional face-to-face evaluation. The potential benefits of automated analysis of digital images of diabetic retinopathy have been shown in existing studies. However, no current computer-based systems can achieve the same level of performance of human experts. This application takes a new perspective in developing a computer-aided system for improved diagnosis of diabetic retinopathy, by exploring novel computational methods for retrieving clinically-relevant images from archived database with prior diagnosis information, for a given novel image. Images are considered as being clinically relevant if they contain the same types of lesions with similar severity levels. Research and development on content-based retinal image search/retrieval is still in its infancy, with only limited success, largely due to the challenge of explicitly coding expert-knowledge into a computational algorithm. To deal with the challenge, this research project takes a distinctly different approach engaging a machine-learning approach, where a labeled image set is used to train a computer algorithm for analyzing other new images, with the focus of training on similarity in clinical relevance instead of image features. The training is enabled in part by the investigators' existing research on computer-based lesion simulation. One specific aim of the research is to build a content-based image retrieval system that can provide a clinician with instant reference to archival images that are clinically relevant to the image under diagnosis. This is an innovative way of exploiting vast expert knowledge hidden in libraries of previously-diagnosed digital images of diabetic retinopathy for a clinician's improved performance in diagnosis. Another specific aim is to build an image information management system for diabetic retinopathy that supports the deployment of the retrieval system in a realistic clinical setting. In addition to the retrieval system, the direct outcome of the research also includes automated evaluation algorithms for diabetic retinopathy images with potentially improved performance compared with existing methods. In particular, the design of the proposed work allows different configurations of the resultant system according to the specific needs of a physician. PUBLIC HEALTH RELEVANCE: This project develops a computer-based system for improved diagnosis of diabetic retinopathy, a vision- threatening complication in people with diabetes. Early detection and timely intervention or treatment can reduce the incidence of blindness, and the computer-based system can potentially improve not only the speed but also the accuracy in diagnosing or screening patients with diabetic retinopathy.