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: 8703708
• 负责人: Sina Farsiu
• 金额: $ 37.97万
• 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 2017-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8703708
• 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; improved; innovation; macula; macular edema; new technology; novel; open source; 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（由申请人提供）：目前还没有成熟的方法来识别和评估糖尿病性黄斑水肿（DME）病理生物学的机制，糖尿病性黄斑水肿是美国工作年龄人口失明的主要原因之一。因此，病理生理学的发展。 - DME 的特异性治疗药物有限，并且针对个体患者的个性化治疗选择仍然是主观的。我们的长期目标是开发利用视网膜成像技术将 DME 患者分层的自动化方法。反过来，我们期望根据这些机制进行分组将有助于为每位患者选择最佳的个性化治疗。当前的范式单独或共同分离出三种不同的病理生理机制，作为 DME 的影响因素： ）毛细血管内皮细胞功能障碍，b）视网膜神经胶质细胞泵功能障碍，以及c）视网膜色素上皮细胞泵功能障碍我们基于此范例提出两个相互关联的假设：1）荧光素血管造影 (FA) 和 SD-OCT 可以使用自动化算法进行定量分析，以推断具体的疾病机制。在 FA 上，弥漫性与局灶性渗漏面积 (D/F) 的比率将反映两种泵功能障碍 DME 亚型的相对优势。与毛细血管渗漏亚型相比，在 SD-OCT 上，黄斑增厚和其他表明弥漫性和局灶性 DME 的形态特征可以通过层分割来识别。 D/F 比和 SD-OCT 定量分析将作为治疗反应的预测生物标志物。更具体地说，弥漫性 DME 的 FA 和 SD-OCT 标志物对药物治疗反应更好，而局灶性 DME 标志物对局灶性反应更好。我们将通过追求以下三个具体目标来测试这些假设： 目标 I：开发自动化软件来量化 FA 和 SD-OCT 上的 DME 亚型成像生物标志物 目标 II：使用存档的 DME 案例来完善和验证开发的自动化算法。在目标 I. 目标 III：进行初步试验以确定 D/F 比值在预测“治疗方案”中抗 VEGF 反应性的功效。该项目意义重大，因为针对疾病亚型的个性化治疗的需求尚未得到满足。该项目将提供基于 FA 和 SD-OCT 的客观 DME 分型方法以及对不同 DME 机制的推理支持。该项目具有创新性我们将利用新颖的数学概念并开发算法，在临床环境中以自动化方式可靠地测量 DME 成像生物标志物。开发的软件将免费向公众分发。预计将成为战士们用于个性化治疗选择或图像读取中心用于对新 DME 药物临床试验进行患者分层的标准方法。 我们新颖的图像处理算法及其基础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。