鬼影卷积自适应视网膜血管分割算法

Ghost convolution adaptive retinal vessel segmentation algorithm

针对视网膜血管分割存在主血管轮廓模糊、微细血管断裂和视盘边界误分割等问题,提出一种鬼影卷积自适应视网膜血管分割算法。算法一是用鬼影卷积替代神经网络中普通卷积,鬼影卷积生成丰富的血管特征图,使目标特征提取充分进行。二是将生成的特征图进行自适应融合并输入至解码层分类,自适应融合能够多尺度捕获图像信息和高质量保存细节。三是在精确定位血管像素与解决图像纹理损失过程中,构建双路径注意力引导结构将网络底层特征图与高层特征图有效结合,提高血管分割准确率。同时引入Cross-Dice Loss函数来抑制正负样本不均问题,减少因血管像素占比少而引起的分割误差,在DRIVE与STARE数据集上进行实验,其准确率分别为96.56%和97.32%,敏感度分别为84.52%和83.12%,特异性分别为98.25%和98.96%,具有较好的分割效果。

In order to solve the problems in retinal vessel segmentation,such as blurred main vessel profile,broken microvessels,and missegmented optic disc boundary,a ghost convolution adaptive retinal vessel segmentation algorithm is proposed.The first algorithm uses ghost convolution to replace the common convolution in neural network,and the ghost convolution generates rich vascular feature maps to make the target feature extraction fully carried out.Secondly,the generated feature images are adaptive fusion and input to the decoding layer for classification.Adaptive fusion can capture image information at multiple scales and save details with high quality.Thirdly,in the process of accurately locating vascular pixels and solving image texture loss,a dual-pathway attention guiding structure is constructed to effectively combine the feature map at the bottom and the feature map at the top of the network to improve the accuracy of vascular segmentation.At the same time,Cross-Dice Loss function was intro-duced to suppress the problem of uneven positive and negative samples and reduce the segmentation error caused by the small proportion of vascular pixels.Experiments were conducted on DRIVE and STARE datasets.The accuracy was 96.56%and 97.32%,the sensitivity was 84.52%and 83.12%,and the specificity was 98.25%and 98.96%,respectively,which proves the good segmentation effect.