Optic disc (OD) and optic cup (OC) regional parameters are of utmost importance in the early diagnosis of glaucoma. Improving the accuracy of OD/OC segmentation results and parameter extraction in colour fundus images plays a very important role in early glaucoma screening. To improve the accuracy and inference speed of fundus image segmentation, an algorithm for fundus image segmentation based on an attention U-Net with transfer learning is proposed in this paper. First, an attention gate was added between the encoder and decoder of U-Net to focus on the target areas, thus forming the architecture of the attention U-Net. Then, after the network had been trained on the DRIONS-DB dataset to partially obtain the weights of the encoder, it was trained on the Drishti-GS dataset to further modify the weights. Finally, the trained attention U-Net model incorporating transfer learning was used to segment fundus images. OD/OC extraction using this method shows obvious advantages in model parameter quantity and inference time compared with existing algorithms, the parameter quantity is much smaller than that of existing algorithms, and the model inference time is 0.33 s, representing a reduction of more than 50%. The proposed method can be applied to a fundus image dataset with only a small number of labels. Whilst offering fast OD/OC segmentation, it also guarantees a relatively high segmentation accuracy.

中文翻译：

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结合转移学习的注意力 U-Net 在视盘和杯子分割中的应用

视盘 (OD) 和视杯 (OC) 区域参数在青光眼的早期诊断中至关重要。提高彩色眼底图像中OD/OC分割结果和参数提取的准确性在早期青光眼筛查中起着非常重要的作用。为了提高眼底图像分割的准确性和推理速度，本文提出了一种基于attention U-Net和迁移学习的眼底图像分割算法。首先，在 U-Net 的编码器和解码器之间添加了一个注意力门来关注目标区域，从而形成了注意力 U-Net 的架构。然后，在 DRIONS-DB 数据集上训练网络以部分获得编码器的权重后，在 Drishti-GS 数据集上训练以进一步修改权重。最后，结合迁移学习的训练注意力 U-Net 模型用于分割眼底图像。使用该方法提取OD/OC与现有算法相比，在模型参数量和推理时间上具有明显优势，参数量远小于现有算法，模型推理时间为0.33 s，减少了50多%。所提出的方法可以应用于只有少量标签的眼底图像数据集。在提供快速 OD/OC 分割的同时，也保证了相对较高的分割精度。参数量远小于现有算法，模型推理时间为0.33 s，减少50%以上。所提出的方法可以应用于只有少量标签的眼底图像数据集。在提供快速 OD/OC 分割的同时，也保证了相对较高的分割精度。参数量远小于现有算法，模型推理时间为0.33 s，减少50%以上。所提出的方法可以应用于只有少量标签的眼底图像数据集。在提供快速 OD/OC 分割的同时，也保证了相对较高的分割精度。