基于线性化注意力和双重注意力的视杯盘分割模型

Optic cup and disc segmentation model based on linear attention and dual attention

青光眼是致盲性眼病之一,视杯盘比是筛查青光眼的主要依据,因此准确分割视杯盘具有重要意义。本文提出一种基于线性化注意力和双重注意力的视杯盘分割模型。首先,根据视盘特性定位裁剪感兴趣区域。其次,引入线性化注意力的残差网络-34(ResNet-34)作为特征提取网络。最后,通过线性化注意力的输出特征生成通道和空间双重注意力权重,用于校准解码器输出特征获取视杯盘分割图像。实验结果表明,所提模型在视神经头分割的视网膜图像(DRISHTI-GS)数据集中,视盘、视杯交并比分别为0.962 3、0.856 4;用于视神经评估的开放式视网膜图像-V3(RIM-ONE-V3)数据集中,视盘、视杯交并比分别为0.956 3、0.784 4。所提模型优于对比算法,在青光眼的早期筛查中具有一定的医学价值。此外,本文利用知识蒸馏技术生成两种规模更小的模型,有利于将模型应用于嵌入式设备。

Glaucoma is one of blind causing diseases.The cup-to-disc ratio is the main basis for glaucoma screening.Therefore,it is of great significance to precisely segment the optic cup and disc.In this article,an optic cup and disc segmentation model based on the linear attention and dual attention is proposed.Firstly,the region of interest is located and cropped according to the characteristics of the optic disc.Secondly,linear attention residual network-34(ResNet-34)is introduced as a feature extraction network.Finally,channel and spatial dual attention weights are generated by the linear attention output features,which are used to calibrate feature map in the decoder to obtain the optic cup and disc segmentation image.Experimental results show that the intersection over union of the optic disc and cup in Retinal Image Dataset for Optic Nerve Head Segmentation(DRISHTI-GS)dataset are 0.9623 and 0.8564,respectively,and the intersection over union of the optic disc and cup in retinal image database for optic nerve evaluation(RIM-ONE-V3)are 0.9563 and 0.7844,respectively.The proposed model is better than the comparison algorithm and has certain medical value in the early screening of glaucoma.In addition,this article uses knowledge distillation technology to generate two smaller models,which is beneficial to apply the models to embedded device.