IEEE Trans Med Imaging. 2018 Nov;37(11):2493-2501. doi: 10.1109/TMI.2018.2837012. Epub 2018 May 15.
Glaucoma is a chronic eye disease that leads to irreversible vision loss. Most of the existing automatic screening methods first segment the main structure and subsequently calculate the clinical measurement for the detection and screening of glaucoma. However, these measurement-based methods rely heavily on the segmentation accuracy and ignore various visual features. In this paper, we introduce a deep learning technique to gain additional image-relevant information and screen glaucoma from the fundus image directly. Specifically, a novel disc-aware ensemble network for automatic glaucoma screening is proposed, which integrates the deep hierarchical context of the global fundus image and the local optic disc region. Four deep streams on different levels and modules are, respectively, considered as global image stream, segmentation-guided network, local disc region stream, and disc polar transformation stream. Finally, the output probabilities of different streams are fused as the final screening result. The experiments on two glaucoma data sets (SCES and new SINDI data sets) show that our method outperforms other state-of-the-art algorithms.
青光眼是一种慢性眼病，可导致不可逆转的视力丧失。现有的大多数自动筛查方法首先对主要结构进行分割，然后计算临床测量值，以进行青光眼的检测和筛查。然而，这些基于测量的方法严重依赖于分割的准确性，而忽略了各种视觉特征。在本文中，我们引入了一种深度学习技术，以便从眼底图像中直接获取与图像相关的附加信息并筛查青光眼。具体来说，我们提出了一种新颖的基于视盘感知的集成网络，用于自动筛查青光眼，该网络集成了全局眼底图像的深层层次上下文和局部视盘区域。分别考虑四个不同层次和模块的深度流，即全局图像流、分割引导网络、局部视盘区域流和视盘极坐标变换流。最后，将不同流的输出概率融合作为最终的筛查结果。在两个青光眼数据集（SCES 和新 SINDI 数据集）上的实验表明，我们的方法优于其他最先进的算法。
