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

描述（由申请方提供）：目前，视神经肿胀的临床评估受到专家主观检眼镜评价的限制，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3D图像分析方法，用于识别最佳3D参数集，以量化视神经水肿随时间的严重程度，并帮助区分根本原因。本申请的总体目标是开发使用谱域光学相干断层扫描（SD-OCT）的策略，以快速准确地确定诊断为视神经乳头水肿的患者的视神经肿胀的严重程度，并确定将视神经乳头水肿与其他引起视神经水肿的疾病区分开的形态学特征。中心假设是，通过3D图像分析技术获得的关于体积和形状参数的信息将提高准确评估视盘水肿的严重程度和原因的能力，而不是使用Frisn量表或当前2D OCT参数对视神经肿胀进行现有的主观检眼镜评估。这项研究的基本原理是，拥有这样的3D参数将大大改善视盘肿胀的评估方式。具体目标如下：1.开发和评估用于计算SD-OCT中肿胀视神经乳头的新体积和形状参数的方法。这将通过改进和评估我们在视盘肿胀患者的SD-OCT体积中的新型3D基于图形的分割算法来完成。2.确定与视神经乳头水肿患者严重程度临床测量最佳相关的SD-OCT参数，并制定连续的严重程度量表。这将通过使用机器学习方法将SD-OCT参数与专家定义的Frisn量表等级（基于眼底的严重程度测量）相关联来实现。预计体积3D参数将比2D参数更密切地与临床测量相关，并将提供连续的严重程度量表。3.识别将视乳头水肿与其他视盘肿胀原因（或明显视盘肿胀，如假性视乳头水肿）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，特别是那些附近的布鲁赫膜开口，将有助于在自动分化过程中。该方法是创新的，因为申请人开发的3D图像分析方法能够新颖地确定3D体积和形状参数，并且代表了对使用定性图像信息和2D OCT图像信息来评估视盘肿胀的现状的显著改进。这项研究意义重大，因为它将有助于建立一种急需的替代方法和更客观的方法来评估视盘肿胀的严重程度和原因。

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.