Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (6A): 220400273-6.doi: 10.11896/jsjkx.220400273

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Ultrasonic Image Segmentation Based on SegFormer

YANG Jingyi1, LI Fang1,2, KANG Xiaodong1, WANG Xiaotian1, LIU Hanqing1, HAN Junling1   

  1. 1 School of Medical Image,Tianjin Medical University,Tianjin 300202,China;
    2 Beijing Chemical Occupational Disease Control Hospital,Beijing 100093,China
  • Online:2023-06-10 Published:2023-06-12
  • About author:YANG Jingyi,born in 1999,undergra-duates.Her main research interests include medical imaging diagnosis and so on. KANG Xiaodong,born in 1964,Ph.D,professor.His main research interests include medical image processing and medical information system integration.
  • Supported by:
    Beijing-Tianjin-Hebei Collaborative Innovation Project(17YEXTZC00020).

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Abstract: Ultrasonic image segmentation is not only an important part of medical image processing,but also a common technical means of clinical diagnosis.In this paper,the SegFormer network model is proposed to realize the accurate segmentation of medical ultrasound images.On the one hand,the ultrasonic label image is transformed into a single channel and processed by binarization to complete the preprocessing of the data set image;on the other hand,the pre-training model is loaded into the pre-training model to fine-tune the trained model parameters,and a random gradient descent optimizer with momentum is selected to accelerate the convergence speed and reduce the oscillation.Experimental results show that,compared with FCN,UNet and DeepLabV3,all the evaluation indexes of the proposed model are the best in the breast nodule ultrasound image data set,and the evaluation indexes of mIoU,Acc,DSC and Kappa is 81.32%,96.22%,88.91% and 77.85% respectively.The experimental results also show that the model is robust in different ultrasonic image data sets.

Key words: SegFormer, Image segmentation, Ultrasonic image, Transformer

CLC Number: 

  • TP391
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