Abstract: Traditional cervical ultrasound nerve detection algorithms have low detection sensitivity, a large number of false positives, and insufficient utilization of low-level features. However, the number of ultrasound images of the neck is small, and the edges are blurred and sensitive to noise. Therefore, an improved U-Net branch fusion algorithm is proposed. It improves the loss function to obtain high-quality candidate samples, replaces the ordinary convolutional layer in the original structure with a multi-scale convolution structure to enhance feature extraction, and combines expanded convolution to replace middle and deep pooling operations so as to improve the utilization of low-level features. The performance of the proposed algorithm is verified through comparative experiments. The experimental results show that, compared with the traditional U-Net and SegNet convolution networks, the proposal improves the small-size ultrasonic neural segmentation effect by nearly 9% and 17% respectively, and the segmentation accuracy is higher for normal-size and small-size neural segmentation. 

Key words: cervical ultrasound nerve detection, multiscale, weighted loss function, convolutional neural network