Image analytics prediction of corneal keratoplasty failure

图像分析在角膜移植失败预测中的应用 摘要 我们将创建专门的图像分析软件来预测角膜移植(穿透性、内皮)失败- URE来自镜面反射的角膜内皮细胞(EC)图像。角膜成形术是最常见的组织 移植，大约10%的失败率，导致失明，患者不适/焦虑，并重复角化- 与最初的手术相比，整形手术失败的几率更高。有了成功的预测图像分析， 我们将能够识别有风险的移植角膜，并可能对它们进行更积极的治疗 局部应用皮质类固醇或其他预防失败的措施。由于功能性内皮细胞(EC)层 维持透明角膜所必需的活性离子泵驱动的液体重新分配所必需的 图像已经被分析为角膜健康的指标。正常EC层表现出较高的细胞密度 排列成以规则为主的六角形阵列。我们将在使用现有的量化双 EC图像中的标志物(EC密度、细胞面积变异系数和六角形)用于评估 角膜健康。我们将计算与局部和远程细胞错乱相关的附加图像特征， 与角膜移植排斥反应相关的图像属性，以及来自计算机视觉的传统特征。包括这个 这些特征的组合将为机器学习分类器提供丰富的输入，旨在预测未来的输出- 来了(例如，失败或没有失败)。我们将应用这些方法来收集大量经过精心管理的数据，这些数据来自于 美国国立卫生研究院资助的凯斯西储大学(CWRU)的研究和Neth-Net之前的研究- 埃尔兰创新眼科手术研究所(NIIOS)。我们的团队由图像处理专家组成，哦- 眼科专家和CWRU眼科和视觉科学系以及大学的工作人员 医院(UH)眼科研究所的角膜图像分析阅读中心(CIARC)，以严谨而闻名- 常规定量生物标志物的OU、高度重复性评估在大量的多个 机构临床试验。我们的共同目标将是确定这种对欧共体的“第二代”分析是否会影响-- 年龄可导致角膜移植失败的预测。如果成功，这个项目将导致软件可以 翻译为支持研究和临床实践。

Image analytics for prediction of keratoplasty failure Summary We will create specialized image analytics software for prediction of keratoplasty (penetrating, endothelial) fail- ure from specular-reflection corneal endothelial cell (EC) images. Keratoplasties are the most common tissue transplant, with roughly a 10% failure rate, leading to blindness, patient discomfort/anxiety, and repeat kerato- plasties with a higher chance for failure than the initial procedure. With successful predictive image analytics, we will be in a position to identify transplanted corneas at risk and possibly treat them more aggressively with topical corticosteroids or other measures to prevent failure. Since a functional endothelial cell (EC) layer is necessary for the active ionic-pump-driven redistribution of fluid necessary to maintain the clear cornea, EC images have been analyzed as an indicator of cornea health. The normal EC layer exhibits high cell density arranged in a predominantly regular, hexagonal array. We will build on the use of existing quantitative bi- omarkers from EC images (EC density, coefficient of variation of cell areas, and hexagonality) used to evaluate cornea health. We will compute additional image features associated with local and long-range cell disarray, image attributes relevant to keratoplasty rejection, and traditional features from computer vision. Including this combination of features will provide rich inputs to machine-learning classifiers aimed at predicting future out- comes (e.g., failure or no failure). We will apply methods to a large aggregation of well-curated data from pre- vious NIH-funded studies at Case Western Reserve University (CWRU) and from previous studies at the Neth- erlands Institute for Innovative Ocular Surgery (NIIOS). Our team consists of image processing experts, oph- thalmologists, and staff from the CWRU Department of Ophthalmology and Visual Sciences and University Hospitals (UH) Eye Institute’s Cornea Image Analysis Reading Center (CIARC), which is well-known for rigor- ous, highly repeatable assessment of conventional quantitative biomarkers in a large number of multi- institutional clinical trials. Together, our goal will be to determine if this “second generation” analysis of EC im- ages can lead to prediction of keratoplasty failure. If successful, this project will lead to software which can be translated to support research and clinical practice.