融合金字塔切分注意力模块的视杯视盘分割

视杯和视盘的垂直直径比是青光眼在临床诊断中的重要指标，为了更加准确地测量杯盘比，针对视网膜眼底图像中的视盘和视杯分割精度的问题，提出了一个改进后的端到端的U型卷积神经网络框架，采用Resnet 34作为新的编码部分，并在每一个编码层的末端引入金字塔切分注意力PSA模块以提取更多的有效特征信息。同时使用1×1卷积代替3×3卷积来简化解码结构，并且使用一个3×3卷积与一个通过跳跃连接的1×1卷积结构取代跳跃连接。该网络模型在内部数据集上完成训练后，在DRISHTI-GS数据集进行测试，对视盘和视杯的分割结果在Dice和IOU上分别表现为97.61%和95.32%，92.91%和86.75%，证明了该模型具有良好的泛化性。

An optic cup and disc segmentation network with pyramid split attention module

The ratio of the vertical diameter of the optic cup to the optic disc is an important indicator in the clinical diagnosis of glaucoma. To measure the cup-to-disc ratio more accurately, an improved U-shaped convolutional neural network framework was proposed for the problem of segmentation accuracy of the optic disc and optic cup in retinal fundus images. Resnet 34 was used as the new encoder part and a pyramid squeeze attention module at the end of each encoder layer was introduced to extract more valid feature information. A 1×1 convolution was also used instead of 3×3 convolution to simplify the decoding structure, and a 3×3 convolution with a 1×1 convolution structure via a skip connection was used instead of a skip connection. The network model was tested on the DRISHTI-GS dataset after completing training on the in-house dataset, and the segmentation results for the optic disc and optic cup performed 97.61% and 95.32%, 92.91% and 86.75% on the Dice and IOU respectively, demonstrating the good generalization properties of the model.