Accurate segmentation of corneal layers depicted on optical coherence tomography (OCT) images is very helpful for quantitatively assessing and diagnosing corneal diseases (e.g., keratoconus and dry eye). In this study, we presented a novel boundary-guided convolutional neural network (CNN) architecture (BG-CNN) to simultaneously extract different corneal layers and delineate their boundaries. The developed BG-CNN architecture used three convolutional blocks to construct two network modules on the basis of the classical U-Net network. We trained and validated the network on a dataset consisting of 1,712 OCT images acquired on 121 subjects using a 10-fold cross-validation method. Our experiments showed an average dice similarity coefficient (DSC) of 0.9691, an intersection over union (IOU) of 0.9411, and a Hausdorff distance (HD) of 7.4423 pixels. Compared with several other classical networks, namely U-Net, Attention U-Net, Asymmetric U-Net, BiO-Net, CE-Net, CPFnte, M-Net, and Deeplabv3, on the same dataset, the developed network demonstrated a promising performance, suggesting its unique strength in segmenting corneal layers depicted on OCT images.

光学相干断层扫描 (OCT) 图像上描绘的角膜层的准确分割对于定量评估和诊断角膜疾病（例如、圆锥角膜和干眼症）。在这项研究中，我们提出了一种新颖的边界引导卷积神经网络 (CNN) 架构 (BG-CNN)，以同时提取不同的角膜层并描绘它们的边界。开发的 BG-CNN 架构在经典 U-Net 网络的基础上使用三个卷积块构建两个网络模块。我们使用 10 倍交叉验证方法在由 121 名受试者采集的 1,712 张 OCT 图像组成的数据集上训练和验证网络。我们的实验表明平均骰子相似系数 (DSC) 为 0.9691，联合交集 (IOU) 为 0.9411，Hausdorff 距离 (HD) 为 7.4423 像素。与其他几个经典网络，即 U-Net、Attention U-Net、Asymmetric U-Net、BiO-Net、CE-Net、CPFnte、M-Net 和 Deeplabv3 在同一数据集上相比，