대한의용생체공학회:의공학회지 (Journal of Biomedical Engineering Research)

  • 제45권4호
  • /
  • Pages.139-147
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  • 2024
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  • 1229-0807(pISSN)
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  • 2288-9396(eISSN)
대한의용생체공학회 (The Korean Society of Medical and Biological Engineering)
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Whole Spine X-ray 영상에서 척추 영역 분할을 위한 HR-Net 성능 최적화에 관한 연구

Research on the Performance Optimization of HR-Net for Spinal Region Segmentation in Whole Spine X-ray Images

  • 유한범 (을지대학교 일반대학원 방사선학과) ;
  • 황호성 (을지대학교 인공지능 융합 시스템 연구소) ;
  • 김동현 (을지대학교 인공지능 융합 시스템 연구소) ;
  • 오희주 (을지대학교 일반대학원 방사선학과) ;
  • 김호철 (을지대학교 일반대학원 방사선학과)
  • Han Beom Yu (Department of Radiological, Graduate School, Eulji University) ;
  • Ho Seong Hwang (Machine Intelligence Convergence System, Eulji University ) ;
  • Dong Hyun Kim (Machine Intelligence Convergence System, Eulji University ) ;
  • Hee Jue Oh (Department of Radiological, Graduate School, Eulji University) ;
  • Ho Chul Kim (Department of Radiological, Graduate School, Eulji University)
  • 투고 : 2024.04.05
  • 심사 : 2024.08.12
  • 발행 : 2024.08.31
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초록

This study enhances AI algorithms for extracting spinal regions from Whole Spine X-rays, aiming for higher accuracy while minimizing learning and detection times. Whole Spine X-rays, critical for diagnosing conditions such as scoliosis and kyphosis, necessitate precise differentiation of spinal contours. The conventional manual methodology encounters challenge due to the overlap of anatomical structures, prompting the integration of AI to overcome these limitations and enhance diagnostic precision. In this study, 1204 AP and 500 LAT Whole Spine X-ray images were meticulously labeled, spanning the third cervical to the fifth lumbar vertebrae. We based our efforts on the HR-Net algorithm, which exhibited the highest accuracy, and proceeded to simplify its network architecture and enhance the block structure for optimization. The optimized HR-Net algorithm demonstrates an improvement, increasing accuracy by 2.98% for the AP dataset and 1.59% for the LAT dataset compared to its original formulation. Additionally, the modification resulted in a substantial reduction in learning time by 70.06% for AP images and 68.43% for LAT images, along with a decrease in detection time by 47.18% for AP and 43.07% for LAT images. The time taken per image for detection was also reduced by 47.09% for AP and 43.07% for LAT images. We suggest that the application of the proposed HR-Net in this study can lead to more accurate and efficient extraction of spinal regions in Whole Spine X-ray images. This can become a crucial tool for medical professionals in the diagnosis and treatment of spinal-related conditions, and it will serve as a foundation for future research aimed at further improving the accuracy and speed of spinal region segmentation.

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