Many eye diseases like Diabetic Macular Edema (DME), Age-related Macular Degeneration (AMD), and Glaucoma manifest in the retina, can cause irreversible blindness or severely impair the central version. The Optical Coherence Tomography (OCT), a 3D scan of the retina with high qualitative information about the retinal morphology, can be used to diagnose and monitor changes in the retinal anatomy. Many Deep Learning (DL) methods have shared the success of developing an automated tool to monitor pathological changes in the retina. However, the success of these methods depend mainly on large datasets. To address the challenge from very small and limited datasets, we proposed a DL architecture termed CoNet (Coherent Network) for joint segmentation of layers and fluids in retinal OCT images on very small datasets (less than a hundred training samples). The proposed model was evaluated on the publicly available Duke DME dataset consisting of 110 B-Scans from 10 patients suffering from DME. Experimental results show that the proposed model outperformed both the human experts' annotation and the current state-of-the-art architectures by a clear margin with a mean Dice Score of 88% when trained on 55 images without any data augmentation.

翻译：许多眼部疾病如糖尿病性黄斑水肿（DME）、年龄相关性黄斑变性（AMD）和青光眼均表现于视网膜，可导致不可逆性失明或严重损害中心视力。光学相干断层扫描（OCT）作为一种高分辨率视网膜三维成像技术，可提供视网膜形态的高质量定性信息，用于诊断和监测视网膜解剖结构的变化。诸多深度学习（DL）方法已成功开发出自动化工具以监测视网膜病理变化，然而这些方法的有效性高度依赖大规模数据集。为应对极小规模有限数据集的挑战，我们提出一种名为CoNet（相干网络）的深度学习架构，用于在极少量样本（训练样本不足百例）条件下实现视网膜OCT图像中分层与积液的联合分割。该模型在公开的杜克大学DME数据集上进行评估，该数据集包含来自10名DME患者的110张B-Scan影像。实验结果表明，在未使用任何数据增强条件下，仅用55张图像训练的模型以88%的平均Dice系数显著优于人工标注及当前最优架构，展现出清晰的性能优势。