Skip to content

Convolutional neural networks for the automatic segmentation of lumbar paraspinal muscles in people with low back pain.

  • today d et al. The global burden of low back pain: Estimates from the Global Burden of Disease 2010 study. Ann. Rheum. Dis. 73968–974 (2014).

    ArticleGoogle Scholar

  • O’Sullivan, P., Caneiro, JP, O’Keeffe, M. & O’Sullivan, K. Unraveling the complexity of low back pain. J. Orthop. Sports Phys. Ther. 46932–937 (2016).

    ArticleGoogle Scholar

  • Goubert, D., Oosterwijck, JV, Meeus, M. & Danneels, L. Structural changes of lumbar muscles in non-specific low back pain: A Systematic review. Pain Phys. 19E985–E1000 (2016).

    Google Scholar

  • Crawford, R.J. et al. Geography of lumbar paravertebral muscle fatty infiltration. Spine (Phila Pa 1976) 441294–1302 (2019).

    ArticleGoogle Scholar

  • Kjaer P., Bendix T., Sorensen JS, Korsholm L. & Leboeuf-Yde C. Are MRI-defined fat infiltrations in the multifidus muscles associated with low back pain?. BMC Med. 52 (2007).

    ArticleGoogle Scholar

  • Teichtahl, A.J. et al. Fat infiltration of paraspinal muscles is associated with low back pain, disability, and structural abnormalities in community-based adults. Spine J. fifteen1593–1601 (2015).

    ArticleGoogle Scholar

  • Berry, DB et al. Methodological considerations in region of interest definitions for paraspinal muscles in axial MRIs of the lumbar spine. BMC Musculoskelet. Disord. 19135 (2018).

    ArticleGoogle Scholar

  • Crawford, RJ, Cornwall, J., Abbott, R. & Elliott, JM Manually defining regions of interest when quantifying paravertebral muscles fatty infiltration from axial magnetic resonance imaging: a proposed method for the lumbar spine with anatomical cross-reference. BMC Musculoskelet. Disord. 1825 (2017).

    ArticleGoogle Scholar

  • Hu, Z.-J. et al. An assessment of the intra- and inter-reliability of the lumbar paraspinal muscle parameters using CT scan and magnetic resonance imaging. Spine (Phila Pa 1976) 1976(36), E868–E874 (2011).

    ArticleGoogle Scholar

  • Gross, C. et al. Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks. neuroimaging 184901–915 (2018).

    ArticleGoogle Scholar

  • Dam, EB, Lillholm, M., Marques, J. & Nielsen, M. Automatic segmentation of high- and low-field knee MRIs using knee image quantification with data from the osteoarthritis initiative. J. Med. Imaging two024001 (2015).

    ArticleGoogle Scholar

  • Crawford RJ, Fortin M, Weber KA, Smith A, & Elliott JM Are magnetic resonance imaging technologies crucial to our understanding of spinal conditions?. J. Orthop. Sports Phys. Ther. 49320–329 (2019).

    ArticleGoogle Scholar

  • Shen, H. et al. A Deep-learning–based, fully automated program to segment and quantify major spinal components on axial lumbar spine magnetic resonance imaging. Phys. Ther. https://doi.org/10.1093/ptj/pzab041 (2021).

    Article PubMed Google Scholar

  • Weber, K.A. et al. Deep learning convolutional neural networks for the automatic quantification of muscle fat infiltration following whiplash injury. Sci.Rep. 97973 (2019).

    ADS Article Google Scholar

  • Ronneberger, O., Fischer, P. & Brox, T. U-Net: Convolutional networks for biomedical image segmentation. reading Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) 9351234–241 (2015).

  • Milletari, F., Navab, N. & Ahmadi, S.-A. V-Net: Fully convolutional neural networks for volumetric medical image segmentation. spinal cord Four. Five304–309 (2016).

    Google Scholar

  • Cornwall, J., Stringer, MD & Duxson, M. Functional morphology of the thoracolumbar transversospinal muscles. Spine (Phila Pa 1976) 36E1053–E1061 (2011).

    ArticleGoogle Scholar

  • Tustison, NJ et al. N4ITK: Improved N3 bias correction. IEEETrans. Med Imaging 291310–1320 (2010).

    ArticleGoogle Scholar

  • Desai, AD, Gold, GE, Hargreaves, BA & Chaudhari, AS Technical considerations for semantic segmentation in MRI using convolutional neural networks. (2019). https://doi.org/10.48550/arXiv.1902.01977.

  • Oktay, O. et al. Attention U-Net: Learning where to look for the pancreas. (2018). https://doi.org/10.48550/arxiv.1804.03999.

  • Isensee, F. et al. Automatic cardiac disease assessment on cine-MRI via time-series segmentation and domain specific features. https://doi.org/10.1007/978-3-319-75541-0 (2017).

  • Liu, S. et al. 3D anisotropic hybrid network: Transferring convolutional features from 2D images to 3D anisotropic volumes. reading Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) 11071 LNCS851–858 (2017).

  • Zettler, N. & Mastmeyer, A. Comparison of 2D vs. 3D U-net organ segmentation in abdominal 3D CT images. 41–50 (2021). https://doi.org/10.48550/arxiv.2107.04062.

  • Milletari, F., Navab, N. & Ahmadi, S.-A. V-Net: Fully convolutional neural networks for volumetric medical image segmentation. proc. – 2016 4th Int. Conf. 3D Vision, 3DV 2016 565–571 (2016).

  • Ioffe, S. & Szegedy, C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. 32nd Int. Conf. Mach. Learn. ICML 2015 1448–456 (2015).

  • Wu, Y. & He, K. Group normalization. Int. J. Comput. Vis. 128742–755 (2018).

    ArticleGoogle Scholar

  • Ulyanov, D., Vedaldi, A. & Lempitsky, V. Instance normalization: The missing ingredient for fast stylization. (2016), https://doi.org/10.48550/arxiv.1607.08022.

  • Hesamian, MH, Jia, W., He, X. & Kennedy, P. Deep learning techniques for medical image segmentation: Achievements and challenges. J. Digit. imaging 32582–596 (2019).

    ArticleGoogle Scholar

  • Micikevicius, P. et al. Mixed precision training. 6th Int. Conf. Learn. Rep. ICLR 2018 – Conf. Track Proc. (2017), https://doi.org/10.48550/arxiv.1710.03740.

  • Ni, R., Meyer, CH, Blemker, SS, Hart, JM & Feng, X. Automatic segmentation of all lower limb muscles from high-resolution magnetic resonance imaging using a cascaded three-dimensional deep convolutional neural network. J. Med. Imaging (Bellingham, Wash.) 61 (2019).

    ArticleGoogle Scholar

  • Shahidi, B. et al. Lumbar multifidus muscle degenerates in individuals with chronic degenerative lumbar spine pathology. J. Orthop. Beef. 352700–2706 (2017).

    CAS Article Google Scholar

  • Fortin, M., Omidyeganeh, M., Battié, MC, Ahmad, O. & Rivaz, H. Evaluation of an automated thresholding algorithm for the quantification of paraspinal muscle composition from MRI images. Biomed. Online Eng. 1661 (2017).

    ArticleGoogle Scholar

  • Hancock, M. J. et al. Risk factors for a recurrence of low back pain. Spine J. fifteen2360–2368 (2015).

    ArticleGoogle Scholar

  • Scheinost, D. et al. Have simple rules for predictive modeling of individual differences in neuroimaging. neuroimaging 19335 (2019).

    ArticleGoogle Scholar

  • Consortium, M. MONAI: Medical Open Network for AI. (2022) 10.5281/ZENODO.6114127.

  • Çiçek, Ö., Abdulkadir, A., Lienkamp, ​​SS, Brox, T. & Ronneberger, O. 3D U-Net: Learning dense volumetric segmentation from sparse annotation. reading Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) 9901 LNCS424–432 (2016).

  • Kerfoot, E. et al. Left-Ventricle quantification using residual U-Net. reading Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) 11395 LNCS371–380 (2019).

  • He, K., Zhang, X., Ren, S. & Sun, J. Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. In 2015 IEEE International Conference on Computer Vision (ICCV) vol. 2015 Inter 1026–1034 (IEEE, 2015).

  • Falk, T. et al. U-Net: Deep learning for cell counting, detection, and morphometry. Nat.Methods 1667–70 (2019).

    CAS Article Google Scholar

  • He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) vols 2016-Decem 770–778 (IEEE, 2016).

  • Loshchilov, I. & Hutter, F. Decoupled weight decay regularization. 7th Int. Conf. Learn. Rep. ICLR 2019 (2017), https://doi.org/10.48550/arxiv.1711.05101.

  • Perone, CS, Calabrese, E. & Cohen-Adad, J. Spinal cord gray matter segmentation using deep dilated convolutions. Sci.Rep. 85966 (2018).

    ADS Article Google Scholar

  • Leave a Reply

    Your email address will not be published.