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Deep learning-based high-accuracy quantitation for lumbar intervertebral disc degeneration from MRI

Author

Listed:
  • Hua-Dong Zheng

    (Shanghai University
    Shanghai Key Laboratory of Intelligent Manufacturing and Robotics)

  • Yue-Li Sun

    (Shanghai University of TCM
    Shanghai Academy of TCM
    Key Laboratory of the Ministry of Education of Chronic Musculoskeletal Disease)

  • De-Wei Kong

    (Shanghai University of TCM)

  • Meng-Chen Yin

    (Shanghai University of TCM
    Key Laboratory of the Ministry of Education of Chronic Musculoskeletal Disease)

  • Jiang Chen

    (Beijing University of Chinese Medicine)

  • Yong-Peng Lin

    (Guangdong Provincial Hospital of Chinese Medicine)

  • Xue-Feng Ma

    (Shenzhen Pingle Orthopedics Hospital)

  • Hong-Shen Wang

    (Guangdong Provincial Hospital of Chinese Medicine)

  • Guang-Jie Yuan

    (Shanghai University
    Shanghai Key Laboratory of Intelligent Manufacturing and Robotics)

  • Min Yao

    (Shanghai University of TCM
    Shanghai Academy of TCM
    Key Laboratory of the Ministry of Education of Chronic Musculoskeletal Disease)

  • Xue-Jun Cui

    (Shanghai University of TCM
    Shanghai Academy of TCM
    Key Laboratory of the Ministry of Education of Chronic Musculoskeletal Disease)

  • Ying-Zhong Tian

    (Shanghai University
    Shanghai Key Laboratory of Intelligent Manufacturing and Robotics)

  • Yong-Jun Wang

    (Shanghai University of TCM
    Shanghai Academy of TCM
    Key Laboratory of the Ministry of Education of Chronic Musculoskeletal Disease)

Abstract

To help doctors and patients evaluate lumbar intervertebral disc degeneration (IVDD) accurately and efficiently, we propose a segmentation network and a quantitation method for IVDD from T2MRI. A semantic segmentation network (BianqueNet) composed of three innovative modules achieves high-precision segmentation of IVDD-related regions. A quantitative method is used to calculate the signal intensity and geometric features of IVDD. Manual measurements have excellent agreement with automatic calculations, but the latter have better repeatability and efficiency. We investigate the relationship between IVDD parameters and demographic information (age, gender, position and IVDD grade) in a large population. Considering these parameters present strong correlation with IVDD grade, we establish a quantitative criterion for IVDD. This fully automated quantitation system for IVDD may provide more precise information for clinical practice, clinical trials, and mechanism investigation. It also would increase the number of patients that can be monitored.

Suggested Citation

  • Hua-Dong Zheng & Yue-Li Sun & De-Wei Kong & Meng-Chen Yin & Jiang Chen & Yong-Peng Lin & Xue-Feng Ma & Hong-Shen Wang & Guang-Jie Yuan & Min Yao & Xue-Jun Cui & Ying-Zhong Tian & Yong-Jun Wang, 2022. "Deep learning-based high-accuracy quantitation for lumbar intervertebral disc degeneration from MRI," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28387-5
    DOI: 10.1038/s41467-022-28387-5
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    1. Bernard Marois & Pierre Syssau, 2006. "Pratiques des banques françaises en termes d'analyse du risque-pays," Revue française de gestion, Lavoisier, vol. 162(3), pages 77-91.
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