Liu Yihao, Carass Aaron, He Yufan, Antony Bhavna J, Filippatou Angeliki, Saidha Shiv, Solomon Sharon D, Calabresi Peter A, Prince Jerry L
Dept. of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.
Dept. of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA.
Biomed Opt Express. 2019 Feb 4;10(3):1064-1080. doi: 10.1364/BOE.10.001064. eCollection 2019 Mar 1.
Optical coherence tomography (OCT) is used to produce high resolution depth images of the retina and is now the standard of care for in-vivo ophthalmological assessment. It is also increasingly being used for evaluation of neurological disorders such as multiple sclerosis (MS). Automatic segmentation methods identify the retinal layers of the macular cube providing consistent results without intra- and inter-rater variation and is faster than manual segmentation. In this paper, we propose a fast multi-layer macular OCT segmentation method based on a fast level set method. Our framework uses contours in an optimized approach specifically for OCT layer segmentation over the whole macular cube. Our algorithm takes boundary probability maps from a trained random forest and iteratively refines the prediction to subvoxel precision. Evaluation on both healthy and multiple sclerosis subjects shows that our method is statistically better than a state-of-the-art graph-based method.
光学相干断层扫描（OCT）用于生成视网膜的高分辨率深度图像，现已成为体内眼科评估的护理标准。它也越来越多地用于评估诸如多发性硬化症（MS）等神经系统疾病。自动分割方法可识别黄斑立方体的视网膜层，提供一致的结果，不存在评分者内和评分者间的差异，并且比手动分割更快。在本文中，我们提出了一种基于快速水平集方法的快速多层黄斑OCT分割方法。我们的框架以一种优化的方法使用轮廓，专门用于整个黄斑立方体的OCT层分割。我们的算法从经过训练的随机森林中获取边界概率图，并将预测迭代细化到亚体素精度。对健康受试者和多发性硬化症受试者的评估表明，我们的方法在统计学上优于一种基于图的先进方法。
