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

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收录情况： ◇ SCIE ◇ 自然指数

作者：
Zheng Hua-Dong[1,2] * ; Sun Yue-Li[3,4,5] * ; Kong De-Wei[3] ; Yin Meng-Chen[3,5] ; Chen Jiang[6] ; Lin Yong-Peng[7] ; Ma Xue-Feng[8] ; Wang Hong-Shen[7] ; Yuan Guang-Jie[1,2] ; Yao Min[3,4,5] ; Cui Xue-Jun[3,4,5] ; Tian Ying-Zhong[9,10] ; Wang Yong-Jun[11,12,13] ;
机构： [1]School of Automation and Mechanical Engineering, Shanghai University, Shanghai 200072, China [2]Shanghai Key Laboratory of Intelligent Manufacturingand Robotics, Shanghai 200072, China [3]Longhua Hospital, Shanghai University of TCM, Shanghai 200032, China [4]Spine Research Institute, ShanghaiAcademy of TCM, Shanghai 200032, China [5]Key Laboratory of the Ministry of Education of Chronic Musculoskeletal Disease, Shanghai 200032, China [6]Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China [7]Guangdong Provincial Hospital of Chinese Medicine, Guangzhou510120, China [8]Shenzhen Pingle Orthopedics Hospital, Shenzhen 518118, China
出处：
DOI： 10.1038/s41467-022-28387-5
ISSN：

摘要：
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.© 2022. The Author(s).

基金：
National Key R&D Program of China (2020YFE0201600) and the National Natural Science Foundation of China (81930116, 81804115, 81873317, and 81730107).
语种：
外文
WOS：
PubmedID：
中科院(CAS)分区：
出版当年[2021]版：
大类 | 1 区 综合性期刊
小类 | 1 区 综合性期刊
最新[2025]版：
大类 | 1 区 综合性期刊
小类 | 1 区 综合性期刊
JCR分区：
出版当年[2020]版：
Q1 MULTIDISCIPLINARY SCIENCES
最新[2023]版：
Q1 MULTIDISCIPLINARY SCIENCES

影响因子： 最新[2023版] 最新五年平均 出版当年[2020版] 出版当年五年平均 出版前一年[2019版] 出版后一年[2021版]

第一作者：
第一作者机构： [1]School of Automation and Mechanical Engineering, Shanghai University, Shanghai 200072, China [2]Shanghai Key Laboratory of Intelligent Manufacturingand Robotics, Shanghai 200072, China
共同第一作者：
通讯作者：
推荐引用方式(GB/T 7714)：
Zheng Hua-Dong,Sun Yue-Li,Kong De-Wei,et al.Deep learning-based high-accuracy quantitation for lumbar intervertebral disc degeneration from MRI.[J].NATURE COMMUNICATIONS.2022,13(1):doi:10.1038/s41467-022-28387-5.
APA：
Zheng Hua-Dong,Sun Yue-Li,Kong De-Wei,Yin Meng-Chen,Chen Jiang...&Wang Yong-Jun.(2022).Deep learning-based high-accuracy quantitation for lumbar intervertebral disc degeneration from MRI..NATURE COMMUNICATIONS,13,(1)
MLA：
Zheng Hua-Dong,et al."Deep learning-based high-accuracy quantitation for lumbar intervertebral disc degeneration from MRI.".NATURE COMMUNICATIONS 13..1(2022)

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