ABSTRACT Vein ultrasound images have the problem of too much noise and poor threshold segmentation. In this regard

this paper proposes a ResNet34-UNet segmentation network model based on the ResNet34 backbone network

using the structural characteristics of the ResNet34 network to ensure that the network can extract sufficient images. And the problem of gradient disappearance and network degradation is effectively avoided

while the network depth of 34 layers maintains a small network scale; using the unique long connection (Skip Connection) module of the U-Net structure

the deep features and shallow features of vein ultrasound images are effectively fused

so that the recognition accuracy of veins can be greatly improved

and the smooth segmentation of vein edges is realized. Use 300 venous ultrasound images as the training set and 200 as the test set

and use random rotation

flip

projection and other operations to enhance the data set. After ten rounds of iterative training

the accuracy of the model was obtained (ACC) reaches 96.3%

which is 5.9% higher than Fully Convolutional Neural Network (FCN) and 5.2% higher than DeepLab v3+. The results show that the vein segmentation method based on ResNet34-UNet can accurately segment the vein ultrasound image

which provides a technical reference for the automatic identification and tracking of the vein under the ultrasound image.