Computer Aided Classification of Diabetic Macular Edema

糖尿病黄斑水肿的计算机辅助分类

• 批准号: 8893997
• 负责人: Sina Farsiu
• 金额: $ 33.25万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8893997
• 关键词: Ablation; Affect; Age; Algorithms; American; Angiography; Archives; Area; Biological Markers; Blindness; Blood Vessels; Blood capillaries; Capillary Endothelial Cell; Cells; Classification; Clinical; Clinical Trials; Computer Assisted; Computer software; Data Set; Development; Diabetic Retinopathy; Diffuse; Disease; Edema; Extracellular Fluid; Extravasation; Eye; Fluorescein; Fluorescein Angiography; Functional disorder; Goals; Image; Image Analysis; Imaging technology; Individual; Intracellular Fluid; Knowledge; Lasers; Lipids; Liquid substance; Measures; Medical Imaging; Methodology; Methods; Microaneurysm; Muller' s cell; Patients; Pattern; Performance; Pharmaceutical Preparations; Pharmacotherapy; Plasma; Population; Process; Prospective Studies; Pump; Reading; Relative (related person); Research; Retina; Retinal; Site; Structure of retinal pigment epithelium; Subgroup; Technology; Testing; Therapeutic; Therapeutic Agents; Vascular Endothelial Growth Factors; Visual Acuity; Work; base; capillary; corticosteroid inhibitor; diabetic; diabetic patient; disorder subtype; image processing; imaging biomarker; improved; innovation; macula; macular edema; new technology; novel; open source; personalized medicine; pilot trial; response; software development; treatment response

DESCRIPTION (provided by applicant): There are currently no well-established methods to identify and evaluate the mechanisms underlying diabetic macular edema (DME) pathobiology, one of the leading causes of blindness among working-age Americans. As such, the development of pathophysiology-specific therapeutic agents for DME is limited, and the selection of therapies personalized for individual patients remains subjective. Our long-term goal is to develop automated methods that exploit retinal imaging technologies to stratify DME patients into subgroups that reflect specific pathophysiological mechanisms. In turn, we expect that subgrouping according to these mechanisms will facilitate an optimal choice of personalized therapy for each patient. The current paradigm isolates three different pathophysiologic mechanisms, independently or together, as contributing factors to DME: a) capillary endothelial cell dysfunction, b) retinal glial cellular pump dysfunction, and c) retinal pigment epithelium cel pump dysfunction. We propose two interrelated hypotheses based on this paradigm: 1) Fluorescein angiography (FA) and SD-OCT can be quantitatively analyzed using automated algorithms to infer the specific disease mechanism. On FA, the diffuse to focal leakage area (D/F) ratio will reflect the relative predominance of the two pump dysfunction DME subtypes versus the capillary leakage subtype. On SD-OCT, macular thickening and other morphological features indicative of diffuse and focal DME can be identified through layer segmentation. 2) Image analysis using both the D/F ratio and quantitative analysis of SD-OCT will serve as predictive biomarkers for therapeutic responses. More specifically, the FA and SD-OCT markers of diffuse DME will respond better to pharmacotherapy, whereas the markers of focal DME will respond better to focal laser. We will test these hypotheses by pursuit of the following three specific aims: Aim I: Develop automated software to quantify DME subtype imaging biomarkers on FA and SD-OCT. Aim II: Use archived DME cases to refine and validate the automated algorithms developed in Aim I. Aim III: Perform a pilot trial to determine the efficacy of the D/F ratio in predicting anti-VEGF responsiveness in a "treatment na�ve" and unbiased population. This project is significant because there is an unmet need for therapies personalized to disease subtype. This project will provide objective DME subtyping methods based on FA and SD-OCT and inferential support for different DME mechanisms. This project is innovative and impactful in terms of new technology and new knowledge. We will utilize novel mathematical concepts and develop algorithms to reliably measure DME imaging biomarkers in an automated fashion in a clinical setting. Developed software will be freely distributed to the public and are expected to become the standard methodology used by clinicians to personalize the choice of therapy or by image reading centers to stratify patients for clinical trials of new DME drugs. Finally, we expect that our novel image processing algorithms and their underlying mathematical frameworks will have an immediate impact on a wide spectrum of medical image processing research applications.

描述（由适用提供）：目前尚无公认的方法来识别和评估糖尿病性黄斑水肿（DME）病理学的机制，这是工人年龄在美国人中失明的主要原因之一。因此，DME的病理生理特异性治疗剂的开发是有限的，并且为个别患者的个性化疗法选择仍然是主观的。我们的长期目标是开发自动化方法，以利用视网膜成像技术将DME患者分类为反映特定病理生理机制的亚组。反过来，我们预计根据这些机制进行亚组将有助于每个患者的个性化治疗方法。当前的范式将三种不同的病理生理机制独立或共同分离为DME的促成因素：a）毛细管内皮细胞功能障碍，b）视网膜胶质细胞泵功能障碍，c）视网膜色素上皮泵泵功能障碍。我们提出了两个基于此范式的相互关联的假设：1）可以使用自动化算法定量分析荧光素血管造影（FA）和SD-OCT，以推断特定的疾病机制。在FA上，扩散到局灶性泄漏区域（D/F）比将反映两个泵功能障碍DME亚型的相对优势与毛细管泄漏亚型的相对占主导地位。在SD-OCT上，可以通过层分割来识别SD-OCT，黄斑增厚和其他指示扩散和局灶性DME的形态特征。 2）使用SD-OCT的D/F比和定量分析的图像分析将作为治疗反应的预测生物标志物。更具体地说，扩散DME的FA和SD-OCT标记将对药物疗法做出更好的反应，而焦点DME的标记对焦点激光器的反应更好。我们将通过追求以下三个特定目标来检验这些假设：目标I：开发自动化软件以量化FA和SD-OCT上的DME亚型成像生物标志物。 AIM II：使用存档的DME案例来完善和验证AIM I III中开发的自动化算法：执行试验试验，以确定D/F比在预测“治疗中神中”和公正人群中预测抗VEGF反应性方面的效率。该项目很重要，因为对疾病亚型的个性化疗法未满足。该项目将基于FA和SD-OCT以及对不同DME机制的推断支持提供客观的DME亚型方法。该项目在新技术和新知识方面具有创新性和影响力。我们将利用新颖的数学概念，并在临床环境中以自动化的方式开发算法来可靠测量DME成像生物标志物。开发的软件将是免费分发给公众的，并有望成为临床医生使用的标准方法，以个性化治疗选择或图像阅读中心，以将患者分类用于新DME药物的临床试验。最后，我们期望 我们新颖的图像处理算法及其基本的数学框架将对广泛的医学图像处理研究应用产生直接影响。