Baamonde Sergio, de Moura Joaquim, Novo Jorge, Charlón Pablo, Ortega Marcos
Department of Computer Science, University of A Coruña, 15071 A Coruña, Spain.
CITIC-Research Center of Information and Communication Technologies, University of A Coruña, 15071 A Coruña, Spain.
Biomed Opt Express. 2019 Jul 16;10(8):4018-4033. doi: 10.1364/BOE.10.004018. eCollection 2019 Aug 1.
Optical coherence tomography (OCT) is a medical image modality that is used to capture, non-invasively, high-resolution cross-sectional images of the retinal tissue. These images constitute a suitable scenario for the diagnosis of relevant eye diseases like the vitreomacular traction or the diabetic retinopathy. The identification of the epiretinal membrane (ERM) is a relevant issue as its presence constitutes a symptom of diseases like the macular edema, deteriorating the vision quality of the patients. This work presents an automatic methodology for the identification of the ERM presence in OCT scans. Initially, a complete and heterogeneous set of features was defined to capture the properties of the ERM in the OCT scans. Selected features went through a feature selection process to further improve the method efficiency. Additionally, representative classifiers were trained and tested to measure the suitability of the proposed approach. The method was tested with a dataset of 285 OCT scans labeled by a specialist. In particular, 3,600 samples were equally extracted from the dataset, representing zones with and without ERM presence. Different experiments were conducted to reach the most suitable approach. Finally, selected classifiers were trained and compared using different metrics, providing in the best configuration an accuracy of 89.35%.
光学相干断层扫描（OCT）是一种医学成像方式，用于无创地获取视网膜组织的高分辨率横截面图像。这些图像为诊断诸如玻璃体黄斑牵拉或糖尿病性视网膜病变等相关眼部疾病提供了合适的场景。视网膜前膜（ERM）的识别是一个重要问题，因为它的存在是诸如黄斑水肿等疾病的症状，会降低患者的视力质量。这项工作提出了一种在OCT扫描中识别ERM存在的自动方法。首先，定义了一组完整且异质的特征来捕捉OCT扫描中ERM的特性。所选特征经过特征选择过程以进一步提高方法效率。此外，对代表性分类器进行了训练和测试，以衡量所提出方法的适用性。该方法使用由专家标记的285张OCT扫描数据集进行了测试。特别是，从数据集中均匀提取了3600个样本，代表有和没有ERM存在的区域。进行了不同的实验以找到最合适的方法。最后，使用不同的指标对所选分类器进行训练和比较，在最佳配置下提供了89.35%的准确率。
