Lang Andrew, Carass Aaron, Sotirchos Elias, Calabresi Peter, Prince Jerry L
Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218.
Proc SPIE Int Soc Opt Eng. 2013 Mar 13;8669. doi: 10.1117/12.2006649.
Optical coherence tomography (OCT) has become one of the most common tools for diagnosis of retinal abnormalities. Both retinal morphology and layer thickness can provide important information to aid in the differential diagnosis of these abnormalities. Automatic segmentation methods are essential to providing these thickness measurements since the manual delineation of each layer is cumbersome given the sheer amount of data within each OCT scan. In this work, we propose a new method for retinal layer segmentation using a random forest classifier. A total of seven features are extracted from the OCT data and used to simultaneously classify nine layer boundaries. Taking advantage of the probabilistic nature of random forests, probability maps for each boundary are extracted and used to help refine the classification. We are able to accurately segment eight retinal layers with an average Dice coefficient of 0.79 ± 0.13 and a mean absolute error of 1.21 ± 1.45 pixels for the layer boundaries.
光学相干断层扫描（OCT）已成为诊断视网膜异常最常用的工具之一。视网膜形态和层厚均可提供重要信息，以辅助这些异常的鉴别诊断。自动分割方法对于提供这些厚度测量至关重要，因为鉴于每次OCT扫描中的数据量巨大，手动描绘每一层都很繁琐。在这项工作中，我们提出了一种使用随机森林分类器进行视网膜层分割的新方法。总共从OCT数据中提取了七个特征，并用于同时对九个层边界进行分类。利用随机森林的概率性质，提取每个边界的概率图并用于帮助优化分类。我们能够准确分割八个视网膜层，层边界的平均骰子系数为0.79±0.13，平均绝对误差为1.21±1.45像素。
