Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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Development and Evaluation of D-Attention Unet Model Using 3D and Continuous Visual Context for Needle Detection in Continuous Ultrasound Images

연속 초음파영상에서의 바늘 검출을 위한 3D와 연속 영상문맥을 활용한 D-Attention Unet 모델 개발 및 평가

  • Lee, So Hee (Dept. Biomedical Engineering, Konyang University) ;
  • Kim, Jong Un (Dept. Biomedical Engineering, Konyang University Graduate School) ;
  • Lee, Su Yeol (Advanced Medical Technology Laboratory, Healcerion Co.) ;
  • Ryu, Jeong Won (Advanced Medical Technology Laboratory, Healcerion Co.) ;
  • Choi, Dong Hyuk (Dept. Biomedical Engineering, Konyang University) ;
  • Tae, Ki Sik (Dept. Biomedical Engineering, Konyang University)
  • Received : 2020.09.03
  • Accepted : 2020.10.25
  • Published : 2020.10.31
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Abstract

Needle detection in ultrasound images is sometimes difficult due to obstruction of fat tissues. Accurate needle detection using continuous ultrasound (CUS) images is a vital stage of treatment planning for tissue biopsy and brachytherapy. The main goal of the study is classified into two categories. First, new detection model, i.e. D-Attention Unet, is developed by combining the context information of 3D medical data and CUS images. Second, the D-Attention Unet model was compared with other models to verify its usefulness for needle detection in continuous ultrasound images. The continuous needle images taken with ultrasonic waves were converted into still images for dataset to evaluate the performance of the D-Attention Unet. The dataset was used for training and testing. Based on the results, the proposed D-Attention Unet model showed the better performance than other 3 models (Unet, D-Unet and Attention Unet), with Dice Similarity Coefficient (DSC), Recall and Precision at 71.9%, 70.6% and 73.7%, respectively. In conclusion, the D-Attention Unet model provides accurate needle detection for US-guided biopsy or brachytherapy, facilitating the clinical workflow. Especially, this kind of research is enthusiastically being performed on how to add image processing techniques to learning techniques. Thus, the proposed method is applied in this manner, it will be more effective technique than before.

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References

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