Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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Deep Learning-based Spine Segmentation Technique Using the Center Point of the Spine and Modified U-Net

척추의 중심점과 Modified U-Net을 활용한 딥러닝 기반 척추 자동 분할

  • Sungjoo Lim (Department of Biomedical Systems Informatics, Yonsei University) ;
  • Hwiyoung Kim (Department of Biomedical Systems Informatics, Yonsei University)
  • 임성주 (연세대학교 의과대학 의생명시스템정보학과) ;
  • 김휘영 (연세대학교 의과대학 의생명시스템정보학과)
  • Received : 2023.04.11
  • Accepted : 2023.04.21
  • Published : 2023.04.30
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Abstract

Osteoporosis is a disease in which the risk of bone fractures increases due to a decrease in bone density caused by aging. Osteoporosis is diagnosed by measuring bone density in the total hip, femoral neck, and lumbar spine. To accurately measure bone density in the lumbar spine, the vertebral region must be segmented from the lumbar X-ray image. Deep learning-based automatic spinal segmentation methods can provide fast and precise information about the vertebral region. In this study, we used 695 lumbar spine images as training and test datasets for a deep learning segmentation model. We proposed a lumbar automatic segmentation model, CM-Net, which combines the center point of the spine and the modified U-Net network. As a result, the average Dice Similarity Coefficient(DSC) was 0.974, precision was 0.916, recall was 0.906, accuracy was 0.998, and Area under the Precision-Recall Curve (AUPRC) was 0.912. This study demonstrates a high-performance automatic segmentation model for lumbar X-ray images, which overcomes noise such as spinal fractures and implants. Furthermore, we can perform accurate measurement of bone density on lumbar X-ray images using an automatic segmentation methodology for the spine, which can prevent the risk of compression fractures at an early stage and improve the accuracy and efficiency of osteoporosis diagnosis.

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