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Abstract：
Automatic and accurate segmentation of optic disc (OD) and optic cup (OC) in fundus images is a fundamental task in computer-aided ocular pathologies diagnosis. The complex structures, such as blood vessels and macular region, and the existence of lesions in fundus images bring great challenges to the segmentation task. Recently, the convolutional neural network-based methods have exhibited its potential in fundus image analysis. In this paper, we propose a cascaded two-stage network architecture for robust and accurate OD and OC segmentation in fundus images. In the first stage, the U-Net like framework with an improved attention mechanism and focal loss is proposed to detect accurate and reliable OD location from the full-scale resolution fundus images. Based on the outputs of the first stage, a refined segmentation network in the second stage that integrates multi-task framework and adversarial learning is further designed for OD and OC segmentation separately. The multi-task framework is conducted to predict the OD and OC masks by simultaneously estimating contours and distance maps as auxiliary tasks, which can guarantee the smoothness and shape of object in segmentation predictions. The adversarial learning technique is introduced to encourage the segmentation network to produce an output that is consistent with the true labels in space and shape distribution. We evaluate the performance of our method using two public retinal fundus image datasets (RIM-ONE-r3 and REFUGE). Extensive ablation studies and comparison experiments with existing methods demonstrate that our approach can produce competitive performance compared with state-of-the-art methods.
摘要：
眼底图像中视盘（OD）和视杯（OC）的自动和准确分割是计算机辅助眼部病理诊断的基本任务。复杂的结构，比如血管和黄斑区，眼底图像中病变的存在给分割任务带来了极大的挑战。最近,基于卷积神经网络的方法在眼底图像分析中显示了其潜力。在本文中,我们提出了一种级联的两级网络体系结构，用于眼底图像中可靠且准确的OD和OC分割。在第一阶段,提出了具有改进的注意力机制和焦点丢失的U-Net框架，以从全分辨率眼底图像中检测准确可靠的OD位置。根据第一级的输出，在第二阶段中，将多任务框架和对抗性学习集成在一起的精细分割网络进一步分别设计用于OD和OC分割。多任务框架是通过同时估计轮廓和距离图作为辅助任务来预测OD和OC掩模，可以保证分割预测中对象的平滑性和形状。引入对抗性学习技术以鼓励分割网络产生与空间和形状分布中的真实标签一致的输出。我们使用两个公共视网膜眼底图像数据集（RIM-ONE-r3和REFUGE）评估我们方法的性能。广泛的消融研究和与现有方法的比较实验表明，与最先进的方法相比，我们的方法可以产生有竞争力的性能。
