Research on SED-UNet Segmentation Method for Neck Ultrasound Image - LIU Mingzhu; FU Cong; SONG Shijie; ZHAO Shoubo (Journal of Harbin University of Science and Technology, 2024)
Ultrasound image is one of the commonly used medical diagnosis methods. Manual interpretation of ultrasound image largely depends on doctors ′ subjective experience and knowledge, which is time-consuming and labor-consuming, and is difficult to meet the needs of rapid and batch clinical diagnosis. Therefore, this paper proposes an image segmentation model SED-UNet based on deep learning and deformable convolution U-Net. The deformable convolution combined with BN and Dropout layer is used to optimize and improve the convolution operation of the original network, improve the network convergence, increase the robustness of the network model and improve the training efficiency of the model. The senet module is used to optimize and improve the jump connection in the decoding stage, improve the segmentation accuracy, and then construct a convolution neural network model suitable for neck ultrasound image segmentation. The test results show that the SED-UNet model proposed in this paper has good performance in the automatic segmentation of neck ultrasound images. The F1 coefficient, accuracy and MIoU parameters are improved by 3. 94% , 7. 61% and 7. 15% respectively compared with the traditional U-Net, and achieve a better segmentation effect from the objective evaluation index.