The detection of the optic disk (OD) and fovea is crucial to the automatic diagnosis based on fundus images. This task is very challenging, especially when varieties of lesions exist. Traditional handcrafted feature-based methods are inaccurate, and deep learning based methods fail easily in abnormal cases. We propose a framework that simultaneously detects the OD and fovea based on deep convolutional neural networks. The original image is first preprocessed and then followed by pseudo label generation. These labels are then fed into a fully convolutional neural network with residual modules for localization of the OD and fovea. Polar transformation is then introduced to the segmentation of the OD. The proposed algorithm achieves a relatively high success rate for OD localization and a 100% success rate for fovea localization on several public datasets. For the segmentation of the OD, the proposed algorithm achieves a low overlapping error on several public datasets. Compared with previous work, the proposed method achieves promising accuracy and robustness, and it is useful for practical applications since it detects the OD and fovea simultaneously and completely.

中文翻译：

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用于视盘和中央凹检测的深度多任务框架

视盘（OD）和中央凹的检测对于基于眼底图像的自动诊断至关重要。这项任务非常具有挑战性，尤其是当存在多种病变时。传统手工制作的基于特征的方法不准确，基于深度学习的方法在异常情况下容易失败。我们提出了一个基于深度卷积神经网络同时检测 OD 和中央凹的框架。首先对原始图像进行预处理，然后进行伪标签生成。然后将这些标签输入一个带有残差模块的全卷积神经网络，用于定位 OD 和中央凹。然后将极坐标变换引入到 OD 的分割中。所提出的算法在多个公共数据集上实现了相对较高的 OD 定位成功率和 100% 的中央凹定位成功率。对于OD的分割，所提出的算法在几个公共数据集上实现了低重叠误差。与之前的工作相比，所提出的方法实现了有希望的准确性和鲁棒性，并且由于它同时完整地检测到 OD 和中央凹，因此对于实际应用很有用。