Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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A Divide-Conquer U-Net Based High-Quality Ultrasound Image Reconstruction Using Paired Dataset

짝지어진 데이터셋을 이용한 분할-정복 U-net 기반 고화질 초음파 영상 복원

  • Minha Yoo (National Institute for Mathematical Sciences) ;
  • Chi Young Ahn (National Institute for Mathematical Sciences)
  • 유민하 (국가수리과학연구소 산업수학연구본부) ;
  • 안치영 (국가수리과학연구소 산업수학연구본부)
  • Received : 2024.05.08
  • Accepted : 2024.06.11
  • Published : 2024.06.30
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Abstract

Commonly deep learning methods for enhancing the quality of medical images use unpaired dataset due to the impracticality of acquiring paired dataset through commercial imaging system. In this paper, we propose a supervised learning method to enhance the quality of ultrasound images. The U-net model is designed by incorporating a divide-and-conquer approach that divides and processes an image into four parts to overcome data shortage and shorten the learning time. The proposed model is trained using paired dataset consisting of 828 pairs of low-quality and high-quality images with a resolution of 512x512 pixels obtained by varying the number of channels for the same subject. Out of a total of 828 pairs of images, 684 pairs are used as the training dataset, while the remaining 144 pairs served as the test dataset. In the test results, the average Mean Squared Error (MSE) was reduced from 87.6884 in the low-quality images to 45.5108 in the restored images. Additionally, the average Peak Signal-to-Noise Ratio (PSNR) was improved from 28.7550 to 31.8063, and the average Structural Similarity Index (SSIM) was increased from 0.4755 to 0.8511, demonstrating significant enhancements in image quality.

Keywords

Acknowledgement

본 연구는 정부의 재원으로 국가수리과학연구소(NIMS)와 한국연구재단(NRF)의 지원을 받아 수행하였음(NIMS-B24910000, NRF-2021R1A2C1010993). 초음파 영상 데이터 획득을 위해 수고해주신 Sonographer 김정 선생님께 감사의 뜻을 전합니다.

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