基于改进 U-Net 网络的眼底血管图像分割研究

针对眼底血管图像存在血管细小、视网膜病变而导致分割精度低的问题,提出了一种引入残差块、级联空洞卷积、嵌入注意力机制的U-Net视网膜血管图像分割模型.首先采用提高视网膜图像分辨率,以点噪声为中心、512为边长裁剪来扩增数据集,然后在U-Net模型中引入残差块,增加像素特征的利用率和避免深层网络的退化;并将U-Net网络的底部替换为级联空洞卷积模块,扩大特征图的感受野,提取更丰富的像素特征;最后在解码器中嵌入注意力机制,加重目标特征的权重,减缓无用信息的干扰.基于CHASE数据集的实验结果表明,所提模型的准确率达到了98.2％,灵敏度达到了81.72％,特异值达到了98.90％,与其他多尺度神经网络方法相比体现了更好的分割效果,充分验证了提出改进的U-Net网络模型能有效提高血管分割精度、辅助确诊血管病变.

Research on fundus blood vessel image segmentation based on improved U-Net network

Aiming at the problem of low segmentation accuracy due to small blood vessels and retinopathy in fundus blood vessel images, a U-Net retinal blood vessel image segmentation model that introduces residual blocks, cascaded cavity convolution, and embedded attention mechanism is proposed. First, increase the resolution of the retinal image, crop the data set with point noise as the center and 512 as the side length, and then introduce residual blocks in the U-Net model to increase the utilization of pixel features and avoid the degradation of deep networks; And replace the bottom of the U-Net network with a cascaded hole convolution module to expand the receptive field of the feature map and extract richer pixel features; finally, the attention mechanism is embedded in the decoder to increase the weight of the target feature and slow down useless information Interference. The experimental results based on the CHASE data set show that the accuracy of the proposed model reaches 98. 2%, the sensitivity reaches 81. 72%, and the singular value reaches 98. 90%. Compared with other multi-scale neural network methods, it embodies better segmentation results, and fully verifies that the improved U-Net network model can effectively improve the accuracy of blood vessel segmentation and assist in the diagnosis of vascular disease.