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

Hua-Dong Zheng, Yue-Li Sun, Kong De-Wei, 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 () and Yong-Jun Wang ()
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Hua-Dong Zheng: Shanghai University
Yue-Li Sun: Shanghai University of TCM
Kong De-Wei: Shanghai University of TCM
Meng-Chen Yin: Shanghai University of TCM
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
Min Yao: Shanghai University of TCM
Xue-Jun Cui: Shanghai University of TCM
Ying-Zhong Tian: Shanghai University
Yong-Jun Wang: Shanghai University of TCM

Nature Communications, 2022, vol. 13, issue 1, 1-12

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-28387-5

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