3D Image Analysis Approach to Determine Severity and Cause of Optic Nerve Edema

3D 图像分析方法确定视神经水肿的严重程度和原因

• 批准号: 8842639
• 负责人: MONA K. GARVIN
• 金额: $ 33.3万
• 依托单位: UNIVERSITY OF IOWA
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-05-01 至 2016-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8842639
• 关键词: Acute; Algorithms; Blindness; Bruch' s basal membrane structure; Clinical; Clinical assessments; Computer software; Computing Methodologies; Data; Development; Diagnosis; Diagnostic Procedure; Dimensions; Disease; Early Diagnosis; Edema; Evaluation; Eye diseases; Fundus; Goals; Graph; Health; Image; Imaging Techniques; Intracranial Hypertension; Knowledge; Machine Learning; Measurement; Measures; Methodology; Methods; Mission; Modality; Monitor; Nerve Fibers; Optic Disk; Optic Nerve; Optical Coherence Tomography; Papilledema; Patients; Process; Public Health; Relative (related person); Research; Resolution; Severities; Shapes; Staging; Structure; Swelling; Techniques; Testing; Thick; Three-Dimensional Image; Time; Vision; Work; base; cost; digital; disability burden; expectation; improved; innovation; instrument; novel; optic nerve disorder

DESCRIPTION (provided by applicant): Currently, the clinical assessment of optic nerve swelling is limited by the subjective ophthalmoscopic evaluation by experts in order to diagnose and differentiate the cause of the optic disc edema. The long-term goal of our research effort is to develop automated 3D image-analysis approaches for the identification of an optimal set of 3D parameters to quantify the severity of optic nerve edema over time and to help differentiate the underlying cause. The overall objective in this application is to develop strategies, using spectral-domain optical coherence tomography (SD-OCT), to rapidly and accurately determine the severity of optic nerve swelling in patients diagnosed with papilledema and to ascertain morphological features that differentiate papilledema from other disorders causing optic nerve edema. The central hypothesis is that information about volumetric and shape parameters obtainable from 3D image analysis techniques will improve the ability to accurately assess the severity and cause of optic disc edema over the existing subjective ophthalmoscopic assessment of optic nerve swelling using the Fris¿n scale or current 2D OCT parameters. The rationale for the proposed research is that having such 3D parameters will dramatically improve the way optic disc swelling is assessed. The following specific aims will be pursued: 1. Develop and evaluate the methodology for computing novel volumetric and shape parameters of a swollen optic nerve head from SD-OCT. This will be completed by refining and evaluating our novel 3D graph-based segmentation algorithms in SD-OCT volumes of patients with optic disc swelling. 2. Identify SD-OCT parameters that optimally correlate with clinical measurements of severity in patients with papilledema and develop a continuous severity scale. This will be accomplished by using machine-learning approaches to relate SD-OCT parameters to expert-defined Fris¿n scale grades (a fundus-based measure of severity). It is anticipated that volumetric 3D parameters will more closely correlate with clinical measures than 2D parameters and will provide a continuous severity scale. 3. Identify SD-OCT parameters that differentiate papilledema from other causes of optic disc swelling (or apparent optic disc swelling, as in pseudopapilledema) and develop a corresponding predictive classifier. Our working hypothesis is that 3D shape parameters, especially those near Bruch's membrane opening, will contribute the most in the automatic differentiation process. The approach is innovative because the 3D image-analysis methodology developed by the applicants enables novel determination of 3D volumetric and shape parameters and represents a significant improvement over the status quo of using qualitative image information and 2D OCT image information for assessing optic disc swelling. The proposed research is significant because it will help to establish a much-needed alternative and more objective method by which to assess the severity and cause of optic disc swelling.

描述（由适用提供）：目前，视神经肿胀的临床评估受到专家的主观眼镜评估的限制，以诊断和区分视盘水肿的原因。我们研究工作的长期目标是开发自动化的3D图像分析方法，以识别一组最佳的3D参数，以量化视神经水肿的严重性，并帮助区分潜在的原因。该应用程序的总体目的是使用光谱域光学相干断层扫描（SD-OCT）制定策略，以快速，准确地确定被诊断患有乳头毛乳头的患者的视神经肿胀的严重程度，并确定形态学特征，并与引起光学湿疹的其他疾病区分开。中心假设是，可以从3D图像分析技术获得的有关体积和形状参数的信息将提高准确评估视盘水肿的严重性和原因，而不是现有的主观眼镜肿胀的现有主观眼镜评估，使用FRIS€n scale或当前的2d Oct参数。拟议的研究的理由是，具有这样的3D参数将显着改善评估视盘肿胀的方式。将追求以下特定目标：1。开发和评估从SD-OCT计算肿胀的视神经头的新型体积和形状参数的方法。这将通过精炼和评估我们的新型3D基于光盘肿胀患者的SD-OCT体积中的基于3D图的分割算法。 2。确定与乳头毛瘤患者严重程度的临床测量最佳相关的SD-OCT参数，并产生连续的严重程度。这将通过使用机器学习方法将SD-OCT参数与专家定义的Fris` n量表等级（基于底面的严重程度测量）联系起来来实现。可以预计，体积3D参数将与临床测量值更紧密地相关，而不是2D参数，并且将提供连续的严重程度。 3。确定将乳头毛乳头与光盘肿胀的其他原因（或明显的视盘肿胀，如假磷灰石中的明显）区分开的SD-OCT参数，并开发相应的预测分类器。我们的工作假设是，3D形状参数，尤其是Bruch膜开口附近的参数，将在自动分化过程中贡献最大。该方法具有创新性，因为该应用程序开发的3D图像分析方法可以使3D体积和形状参数的新确定，并且对使用定性图像信息和2d OCT图像信息的现状进行了显着改善，以评估视盘肿胀。拟议的研究很重要，因为它将有助于建立一种急需的替代方案，更客观的方法，以评估视盘肿胀的严重性和原因。