【摘要】 由于不同数据集的质量、拍摄条件和采集状态的差异，导致模型图像分割效果参差不齐。针对这种情况，提出对抗学习下的眼底图像视盘视杯分割算法，从不同眼底图像数据集中分割视盘(OD)和视杯(OC),在生成对抗网络的基础上，改进生成器网络，加入密集连接块，使网络在更低计算成本、更短训练时间的情况下，获得更优的性能，提高了模型在不同数据集中的泛化能力。实验结果表明，在REFUGE数据集中验证了该算法在分割性能方面的稳定性，同时将算法推广到无须进一步训练就能测试来自不同设备的眼底数据集中均取得了较好的效果。更多还原

【Abstract】 The differences in quality, shooting conditions and acquisition status of different datasets lead to uneven results in model image segmentation. To address this situation, the fundus image optic disc and cup segmentation algorithm under adversarial learning is proposed to segment optic disc(OD) and optic cup(OC) from different fundus image datasets. It improved the generator network based on generating adversarial network by adding densely connected blocks, so that the network could obtain better performance with lower computational cost and shorter training time, and improve the generalization ability of the model in different datasets. Experimental results show that the stability of the algorithm in terms of segmentation performance is verified in REFUGE dataset, and the extension of the algorithm to test fundus datasets from different devices without further training achieves better results.更多还原