Downloads

https://doi.org/10.53941/aim.2024.100003
Zhang, R. A State-of-the-Art Survey of Deep Learning for Lumbar Spine Image Analysis: X-Ray, CT, and MRI. AI Medicine. 2024. doi: https://doi.org/10.53941/aim.2024.100003
Forthcoming Issue
Copyright (c) 2024 by the authors.
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Article

A State-of-the-Art Survey of Deep Learning for Lumbar Spine Image Analysis: X-Ray, CT, and MRI

Ruyi Zhang 1,2,*

1 College of Medicine and Biological Information Engineering, Northeastern University, Chuangxin Road, Shenyang, 110016, Liaoning, China; 2390160@stu.neu.edu.cn

2 Research Institute for Medical and Biological Engineering, Ningbo University, Fenghua Road, Ningbo, 315211, Zhejiang, China

Received: 17 April 2024; Revised: 12 June 2024; Accepted: 22 June 2024; Published: 17 July 2024

 

Abstract: Lumbar spine diseases not only endanger patients' physical health but also bring about severe psychological impacts and generate substantial medical costs. Reliable lumbar spine image analysis is crucial for diagnosing and treating lumbar spine diseases. In recent years, deep learning has rapidly developed in computer vision and medical imaging, with an increasing number of researchers applying it to the field of lumbar spine imaging. This paper studies the current state of research in deep learning applications across various modalities of lumbar spine image analysis, including X-ray, CT, and MRI. We first review the public datasets available for various tasks involving lumbar spine images. Secondly, we study the different models used in various lumbar spine image modalities (X-ray, CT, and MRI) and their applications in different tasks (classification, detection, segmentation, and reconstruction). Finally, we discuss the challenges of using deep learning in lumbar spine image analysis and provide an outlook on research and development prospects.

Keywords:

deep learning convolutional neural network X-ray computed tomography magnetic resonance imaging

References

  1. PlCervin Serrano, S.; González Villareal, D.; Aguilar-Medina, M.; Romero-Navarro, J.G.; Romero Quintana, J.G.; Arámbula Meraz, E.; Osuna Ramírez, I.; Picos-Cárdenas, V.; Granados, J.; Estrada-García, I.; et al. Genetic polymorphisms of interleukin-1 alpha and the vitamin d receptor in mexican mestizo patients with intervertebral disc degeneration. Int. J. Genomics 2014, 2014, 302568.
  2. Hodler, J.; Kubik-Huch, R.A.; von Schulthess, G.K. Musculoskeletal Diseases 2021–2024: Diagnostic Imaging; Springer Cham: Cham, Switzerland, 2021.
  3. Ravindra, V.M.; Senglaub, S.S.; Rattani, A.; Dewan, M.C.; Härtl, R.; Bisson, E.; Park, K.B.; Shrime, M.G. Degenerative lumbar spine disease: estimating global incidence and worldwide volume. Global Spine J. 2018, 8, 784–794.
  4. Heikkinen, J.; Honkanen, R.; Williams, L.; Leung, J.; Rauma, P.; Quirk, S.; Koivumaa-Honkanen, H. Depressive disorders, anxiety disorders and subjective mental health in common musculoskeletal diseases: a review. Maturitas 2019, 127, 18–25.
  5. Hooten, W.M.; Cohen, S.P. Evaluation and treatment of low back pain: a clinically focused review for primary care specialists. Mayo Clin. Proc 2015, 90, 1699–1718.
  6. Russo, F.; De Salvatore, S.; Ambrosio, L.; Vadalà, G.; Fontana, L.; Papalia, R.; Rantanen, J.; Iavicoli, S.; Denaro, V. Does workers’ compensation status affect outcomes after lumbar spine surgery? A systematic review and meta-analysis. Int. J. Environ. Res. Public Health 2021, 18, 6165.
  7. Kim, G.U.; Park, W.T.; Chang, M.C.; Lee, G.W. Diagnostic Technology for Spine Pathology. Asian Spine J. 2022, 16, 764-775.
  8. Tjardes, T.; Shafizadeh, S.; Rixen, D.; Paffrath, T.; Bouillon, B.; Steinhausen, E.S.; Baethis, H. Image-guided spine surgery: State of the art and future directions. Eur. Spine J. 2010, 19, 25–45.
  9. Corona-Cedillo, R.; Saavedra-Navarrete, M.-T.; Espinoza-Garcia, J.-J.; Mendoza-Aguilar, A.-N.; Ternovoy, S.K.; Roldan-Valadez, E. Imaging assessment of the postoperative spine: an updated pictorial review of selected complications. Biomed. Res. Int. 2021, 2021, 9940001.
  10. Ou, X.; Chen, X.; Xu, X.; Xie, L.; Chen, X.; Hong, Z.; Bai, H.; Liu, X.; Chen, Q.; Li, L.; et al. Recent development in x-ray imaging technology: Future and challenges. Research 2021, 2021, 9892152.
  11. Van Reeth, E.; Tham, I.W.; Tan, C.H.; Poh, C.L. Super‐resolution in magnetic resonance imaging: a review. Concepts Magn. Reson. 2012, 40A, 306–325.
  12. Zaitsev, M.; Maclaren, J.; Herbst, M. Motion artifacts in MRI: A complex problem with many partial solutions. J. Magn. Reson. Imaging 2015, 42, 887–901.
  13. O’Mahony, N.; Campbell, S.; Carvalho, A.; Harapanahalli, S.; Velasco Hernandez, G.; Krpalkova, L.; Riordan, D.; Walsh, J. Deep learning vs. traditional computer vision. In Advances in Computer Vision: Proceedings of the 2019 Computer Vision Conference (CVC); Springer: Cham, Switzerland, 2020; Volume 1, pp. 128–144.
  14. LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015, 521, 436–444.
  15. Zhuang, F.; Qi, Z.; Duan, K.; Xi, D.; Zhu, Y.; Zhu, H,; Xiong, H.; He, Q. A comprehensive survey on transfer learning. Proc. IEEE, 2020, 109, 43–76.
  16. Qu, B.; Cao, J.; Qian, C.; Wu, J.; Lin, J.; Wang, L.; Ou-Yang, L.; Chen, Y.; Yan, L.; Hong, Q.; et al. Current development and prospects of deep learning in spine image analysis: a literature review. Quant Imaging Med Surg. 2022 12, 3454,.
  17. Lee, J.; Chung, S.W. Deep learning for orthopedic disease based on medical image analysis: Present and future. Appl. Scie. 2022, 12, 681,.
  18. LeCun, Y.; Bottou, L.; Bengio, Y.; Haffner, P. Gradient-based learning applied to document recognition. Proc. IEEE, 1998, 86, 2278–2324.
  19. Krizhevsky, A.; Sutskever, I.; Hinton, G.E. ImageNet classification with deep convolutional neural networks. Commun. ACM 2017, 60, 84–90.
  20. He, K.; Zhang, X.; Ren, S.; Sun. J. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016; pp. 770–778.
  21. Huang, G.; Liu, Z.; Van Der Maaten, L.; Weinberger, K.Q. Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision And Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017; pp. 4700–4708.
  22. Tan, M.; Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. In Proceedings of International Conference on Machine Learning, Long Beach, CA, USA, 9–15 June 2019; PMLR, pp. 6105–6114.
  23. Devlin, J.; Chang, M.-W.; Lee, K.; Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv 2018, arXiv:1810.04805.
  24. Girshick, R.; Donahue, J.; Darrell, T.; Malik, J. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 23–28 June 2014; pp. 580–587.
  25. Girshick, R. Fast R-CNN. In Proceedings of the IEEE International Conference on Computer Vision, Boston, MA, USA, 7–12 June 2015; pp. 1440–1448.
  26. Ren, S.; He, K.; Girshick, R.; Sun, J. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 2016, 39, 1137–1149.
  27. Redmon, J.; Divvala, S.; Girshick, R.; Farhadi, A. You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016; pp. 779–788.
  28. Redmon, J.. Farhadi, A. YOLO9000: better, faster, stronger. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017; pp. 7263–7271.
  29. Redmon, J. Farhadi, A. YOLOv3: An incremental improvement. ArXiv 2018, arXiv:1804.02767.
  30. Liu, W.; Anguelov, D.; Erhan, D.; Szegedy, C.; Reed, S.; Fu, C.-Y.; Berg, A.C. Ssd: Single shot multibox detector. In Proceedings of Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, 11–14 October 2016; pp. 21–37.
  31. Long, J.; Shelhamer, E.; Darrell, T. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 7–12 June 2015; pp. 3431–3440.
  32. Ronneberger, O.; Fischer, P.; Brox, T. U-net: Convolutional networks for biomedical image segmentation. In Proceedings of Medical Image Computing and Computer-Assisted Intervention–Miccai 2015: 18th International Conference, Munich, Germany, 5–9 October 2015; pp. 234–241.
  33. Badrinarayanan, V.; Kendall, A.; Cipolla, R. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 2017, 39, 2481–2495, 2017.
  34. Chen, L.-C.; Papandreou, G.; Kokkinos, I.; Murphy, K.; Yuille, A.L. Semantic image segmentation with deep convolutional nets and fully connected CRFs. ArXiv 2014, arXiv:1412.7062.
  35. Chen, L.-C.; Papandreou, G.; Kokkinos, I.; Murphy, K.; Yuille, A.L. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 2017, 40, 834–848.
  36. Hossin, M. Sulaiman, M.N. A review on evaluation metrics for data classification evaluations. Int. J. Data Min. Knowl. Manag. Process 2015, 5, 1.
  37. Stuckner, J.; Harder, B.; Smith, T.M. Microstructure segmentation with deep learning encoders pre-trained on a large microscopy dataset. NPJ Comput. Mater. 2022, 8, 200.
  38. Laroca, R.; Cardoso, E.V.; Lucio, D.R.; Estevam, V.; Menotti, D. On the cross-dataset generalization in license plate recognition. arXiv 2022, arXiv:2201.00267.
  39. Duong-Trung, N.; Son, H.X.; Le, H.T.; Phan, T.T. Smart care: integrating blockchain technology into the design of patient-centered healthcare systems. In Proceedings of the 2020 4th International Conference on Cryptography, Security and Privacy, Nanjing, China, 10–12 January 2020; pp. 105–109.
  40. El Alaoui, S. Lindefors, N. Combining time-driven activity-based costing with clinical outcome in cost-effectiveness analysis to measure value in treatment of depression. PloS ONE 2016, 11, e0165389.
  41. Bansal, M.A.; Sharma, D.R.; Kathuria, D.M. A systematic review on data scarcity problem in deep learning: solution and applications. ACM Comput. Surv. 2022, 54, 1–29.
  42. Al-kubaisi, A. Khamiss, N.N. A transfer learning approach for lumbar spine disc state classification. Electronics 2021, 11, 85,.
  43. Lumbar Spine MRI Dataset. Available online: https://data.mendeley.com/datasets/k57fr854j2/2 (accessed on 16 April 2024).
  44. Masood, R.F.; Taj, I.A.; Khan, M.B.; Qureshi, M.A.; Hassan, T. Deep learning based vertebral body segmentation with extraction of spinal measurements and disorder disease classification. Biomed. Signal Process. Control 2022, 71, 103230.
  45. Liawrungrueang, W.; Kim, P.; Kotheeranurak, V.; Jitpakdee, K.; Sarasombath, P. Automatic detection, classification, and grading of lumbar intervertebral disc degeneration using an artificial neural network model. Diagnostics 2023, 13, 663.
  46. Le Van, C.; Bao, L.; Puri, V.; Thao, N.T.; Le, D.-N. Detecting lumbar implant and diagnosing scoliosis from vietnamese X-ray imaging using the pre-trained api models and transfer learning. CMC Comput. Mater. Contin 2021, 66, 17–33.
  47. Spineweb. Available online: http://spineweb.digitalimaginggroup.ca/Index.php?n=Main.Datasets (accessed on 16 April 2024).
  48. MICCAI 2019 Challenge. Available online: https://aasce19.github.io/ (accessed on 16 April 2024).
  49. VerSe2020. Available online: https://osf.io/t98fz/ (accessed on 16 April 2024).
  50. VerSe 2019. Available online: https://osf.io/nqjyw/ (accessed on 16 April 2024).
  51. xVertSeg. Available online: https://lit.fe.uni-lj.si/xVertSeg/database.php (accessed on 16 April 2024).
  52. BUU Spine Dataset. Available online: https://services.informatics.buu.ac.th/spine/ (accessed on 16 April 2024).
  53. Khare, M.R.; Havaldar, R.H. Predicting the anterior slippage of vertebral lumbar spine using Densenet-201. Biomed. Signal Process. Control, 2023, 86, 105115.
  54. Varçın, F.; Erbay, H.; Çetin, E.; Çetin, İ.; Kültür, T. End-to-end computerized diagnosis of spondylolisthesis using only lumbar X-rays. J. Digit. Imaging 2021, 34, 85–95.
  55. Sugiura, A.; Yasuda, E. Algorithmic Attempt of Deflection Angle on The Frontal Lumbar Image By Lateral Lumbar Image Analysis. Int. J. Innov. Sci. Eng. Technol. 2022, 9, 10–19.
  56. Nissinen, T.; Suoranta, S.; Saavalainen, T.; Sund, R.; Hurskainen, O.; Rikkonen, T.; Kröger, H.; Lähivaara, T.; Väänänen, S.P. Detecting pathological features and predicting fracture risk from dual-energy X-ray absorptiometry images using deep learning. Bone Rep. 2021, 14, 101070.
  57. Zhang, B.; Yu, K.; Ning, Z.; Wang, K.; Dong, Y.; Liu, X.; Liu, S.; Wang, J.; Zhu, C.; Yu, Q. Deep learning of lumbar spine X-ray for osteopenia and osteoporosis screening: A multicenter retrospective cohort study. Bone 2020, 140, 115561.
  58. Saravagi, D.; Agrawal, S.; Saravagi, M.; Chatterjee, J.M.; Agarwal, M. Diagnosis of lumbar spondylolisthesis using optimized pretrained CNN models. Comput. Intell. Neurosci. 2022, 2022, doi: 10.1155/2022/7459260.
  59. Kim, T.; Kim, Y.G.; Park, S.; Lee, J.K.; Lee, C.H.; Hyun, S.J.; Kim, C.H.; Kim, K.J.; Chung, C.K. Diagnostic triage in patients with central lumbar spinal stenosis using a deep learning system of radiographs. J. Neurosurg. Spine 2022, 37, 104–111.
  60. Patil, K.A.; Prashanth, K.M.; Ramalingaiah A. Law Texture Analysis for the Detection of Osteoporosis of Lumbar Spine (L1-L4) X-ray Images Using Convolutional Neural Networks. IAENG Int. J. Comput. Sci. 2023,50, 71–85, 2023.
  61. Varçin, F.; Erbay, H.; Çetin, E.; Çetin, İ.; Kültür T. Diagnosis of lumbar spondylolisthesis via convolutional neural networks. In Proceedings of 2019 International Artificial Intelligence and Data Processing Symposium (IDAP), Malatya, Turkey, 21–22 September 2019; pp. 1–4.
  62. Klinwichit, P.; Chinnasarn, K.; Onuean, A.; Limchareon, S.; Lee, S.-H.; Jang, J.-S. The Radiographic view classification and localization of Lumbar spine using Deep Learning Models. In Proceedings of 2022 13th International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Republic of Korea, 19–21 October 2022; 1316–1319.
  63. An, C.-H.; Lee, J.-S.; Jang, J.-S.; Choi, H.-C. Part affinity fields and CoordConv for detecting landmarks of lumbar vertebrae and sacrum in X-ray images. Sensors 2022, 22, 8628.
  64. Nguyen, T.P.; Chae, D.-S.; Park, S.-J.; Kang, K.-Y.; Yoon, J. Deep learning system for Meyerding classification and segmental motion measurement in diagnosis of lumbar spondylolisthesis. Biomed. Signal Process. Control 2021, 65, 102371.
  65. Zhou, S.; Yao, H.; Ma, C.; Chen, X.; Wang, W.; Ji, H.; He, L.; Luo, M.; Guo, Y. Artificial intelligence X-ray measurement technology of anatomical parameters related to lumbosacral stability. Eur. J. Radiol. 2022, 146, 110071.
  66. Ruhan, Sa.; Owens, W.; Wiegand, R.; Studin, M.; Capoferri, D.; Barooha, K.; Greaux, A.; Rattray, R.; Hutton, A.; Cintineo, J.; et al. Intervertebral disc detection in X-ray images using faster R-CNN. In Proceedings of 2017 39th annual international conference of the IEEE engineering in medicine and biology society (EMBC), Jeju Island, Republic of Korea, 11–15 July 2017; pp. 564–567.
  67. Lee, J.H.; Woo, H.J.; Lee, J.H.; Kim, J.I.; Jang, J.S.; Na, Y.C.; Kim, K.R.; Park, T.Y. Comparison of concordance between Chuna manual therapy diagnostic methods (palpation, X-ray, artificial intelligence program) in lumbar spine: an exploratory, cross-sectional clinical study. Diagnostics 2022, 12, 2732.
  68. Kim, K.C.; Cho, H.C.; Jang, T.J.; Choi, J.M.; Seo, J.K. Automatic detection and segmentation of lumbar vertebrae from X-ray images for compression fracture evaluation. Comput. Meth. Programs Biomed. 2021, 200, 105833.
  69. Trinh, G.M.; Shao, H.-C.; Hsieh, K.L.-C.; Lee, C.-Y.; Liu, H.-W.; Lai, C.-W.; Chou, S.-Y.; Tsai, P.-I.; Chen, K.-J.; Chang, F.-C.; et al. LumbarNet: A Deep Learning Network for the Automated Detection of Lumbar Spondylolisthesis From X-Ray Images. Preprints 2022, https://doi.org/10.20944/preprints202206.0043.v1
  70. Chen, X.; Deng, Q.; Wang, Q.; Liu, X.; Chen, L.; Liu, J.; Li, S. Wang M and Cao G Image quality control in lumbar spine radiography using enhanced U-Net neural networks. Front. Public Health 2022, 10, 891766.
  71. Tran, V.L.; Lin, H.-Y.; Liu, H.-W. MBNet: A multi-task deep neural network for semantic segmentation and lumbar vertebra inspection on X-ray images. In Proceedings of the Asian Conference on Computer Vision, Virtual Kyoto, 30 November–4 December 2020.
  72. Kónya, S.; Natarajan, T.S.; Allouch, H.; Nahleh, K.A.; Dogheim, O.Y.; Boehm, H. Convolutional neural network-based automated segmentation and labeling of the lumbar spine X-ray. J. Craniovertebral Junction Spine 2021, 12, 136–143.
  73. Cho, B.H.; Kaji, D.; Cheung, Z.B.; Ye, I.B.; Tang, R.; Ahn, A.; Carrillo, O.; Schwartz, J.T.; Valliani, A.A.; Oermann, E.K.; et al. Automated measurement of lumbar lordosis on radiographs using machine learning and computer vision. Glob. Spine J. 2020, 10, 611–618.
  74. Lin. H.-Y.; Liu, H.-W. Multitask deep learning for segmentation and lumbosacral spine inspection. IEEE Trans. Instrum. Meas. 2022, 71, 1–10.
  75. Ryu, S.M.; Lee, S.; Jang, M.; Koh, J.M.; Bae, S.J.; Jegal, S.G.; Shin, K.; Kim, N. Diagnosis of osteoporotic vertebral compression fractures and fracture level detection using multitask learning with U-Net in lumbar spine lateral radiographs. Comput. Struct. Biotechnol. J. 2023, 21, 3452–3458.
  76. Malatong, Y.; Intasuwan, P.; Palee, P.; Sinthubua, A.; Mahakkanukrauh, P. Deep learning and morphometric approach for Sex determination of the lumbar vertebrae in a Thai population. Med.; Sci. Law 2023, 63, 14–21, 2023.
  77. Yasaka, K.; Akai, H.; Kunimatsu, A.; Kiryu, S.; Abe, O. Prediction of bone mineral density from computed tomography: application of deep learning with a convolutional neural network. Eur. Radiol. 2020, 30, 3549–3557.
  78. Doerr, S.A.; Weber-Levine, C.; Hersh, A.M.; Awosika, T.; Judy, B.; Jin, Y.; Raj, D.; Liu, A.; Lubelski, D.; Jones, C.K.; Sair, H.I.; Theodore, N. Automated prediction of the Thoracolumbar Injury Classification and Severity Score from CT using a novel deep learning algorithm. Neurosurg. Focus 2022, 52, E5.
  79. Lu, H.; Li, M.; Yu, K.; Zhang, Y.; Yu, L. Lumbar spine segmentation method based on deep learning. J. Appl Clin. Med. Phys. 2023, 24, e13996.
  80. Malinda, V.; Lee, D. Lumbar vertebrae synthetic segmentation in computed tomography images using hybrid deep generative adversarial networks. In Proceedings of 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Virtual, 20–24 July 2020; pp. 1327–1330.
  81. Gao, X.; Zheng, G. ACSGRegNet: A Deep Learning-based Framework for Unsupervised Joint Affine and Diffeomorphic Registration of Lumbar Spine CT via Cross-and Self-Attention Fusion. In Proceedings of the 2022 International Conference on Intelligent Medicine and Health, New York, NY, USA, 19–21 August 2022; pp. 57–63.
  82. Greffier, J.; Frandon, J.; Durand, Q.; Kammoun, T.; Loisy, M.; Beregi, J.P.; Dabli, D. Contribution of an artificial intelligence deep-learning reconstruction algorithm for dose optimization in lumbar spine CT examination: A phantom study. Diagn. Interv. Imaging 2023, 104, 76–83.
  83. Morbée, L.; Chen, M.; Herregods, N.; Pullens, P.; Jans, L.B. MRI-based synthetic CT of the lumbar spine: Geometric measurements for surgery planning in comparison with CT. Eur. J. Radiol. 2021, 144, 109999.
  84. Yeoh, H.; Hong, S.H.; Ahn, C.; Choi, J.Y.; Chae, H.D.; Yoo, H.J.; Kim, J.H. Deep learning algorithm for simultaneous noise reduction and edge sharpening in low-dose CT images: a pilot study using lumbar spine CT. Korean J. Radiol. 2021, 22, 1850.
  85. Oura, P.; Korpinen, N.; Machnicki, A.L.; Junno, J.-A. Deep learning in sex estimation from a peripheral quantitative computed tomography scan of the fourth lumbar vertebra—a proof-of-concept study. Forens. Sci. Med. Pathol. 2023, 19, 534–540.
  86. Dheivya, I.; Gurunathan, S.K. Deep Learning Based Lumbar Metastases Detection and Classification from Computer Tomography Images. In Proceedings of 2022 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), Kuala Lumpur, Malaysia, 7–9 December 2022; pp. 398–403.
  87. Fan. G.; Liu, H.; Wang, D.; Feng, C.; Li, Y.; Yin, B.; Zhou, Z.; Gu, X.; Zhang, H.; Lu, Y.; He, S. Deep learning-based lumbosacral reconstruction for difficulty prediction of percutaneous endoscopic transforaminal discectomy at L5/S1 level: A retrospective cohort study. Int. J. Surg. 2020, 82, 162–169.
  88. Janssens, R. Zheng, G. Deep learning based segmentation of lumbar vertebrae from CT images. CAOS 2018, 2, 94–97.
  89. Miyo, R.; Yasaka, K.; Hamada, A.; Sakamoto, N.; Hosoi, R.; Mizuki, M.; Abe, O. Deep-learning reconstruction for the evaluation of lumbar spinal stenosis in computed tomography. Medicine 2023, 102, e33910,.
  90. Chen, G.; Xu, Z. Usage of intelligent medical aided diagnosis system under the deep convolutional neural network in lumbar disc herniation. Appl. Soft Comput. 2021, 111, 107674.
  91. Cheung, J.P.Y.; Kuang, X.; Lai, M.K.L.; Cheung, K.M.; Karppinen, J.; Samartzis, D.; Wu, H.; Zhao, F.; Zheng, Z.; Zhang, T. Learning-based fully automated prediction of lumbar disc degeneration progression with specified clinical parameters and preliminary validation. Eur. Spine J. 2021, 31, 1–9.
  92. Gao, F.; Liu, S.; Zhang, X.; Wang, X.; Zhang, J. Automated Grading of Lumbar Disc Degeneration Using a Push‐Pull Regularization Network Based on MRI. J. Magn. Reson. Imaging 2021, 53, 799–806,.
  93. Grob, A.; Loibl, M.; Jamaludin, A.; Winklhofer, S.; Fairbank, J.C.T.; Fekete, T.; Porchet, F.; Mannion, A.F. External validation of the deep learning system “SpineNet” for grading radiological features of degeneration on MRIs of the lumbar spine.Eur. Spine J. 2022, 2137–2148.
  94. Zhou, Y.; Liu, Y.; Chen, Q.; Gu, G.; Sui, X. Automatic lumbar MRI detection and identification based on deep learning. J. Digit. Imaging 2019, 32, 513–520.
  95. Mushtaq, M.; Akram, M.U.; Alghamdi, N.S.; Fatima, J.; Masood, R.F. Localization and edge-based segmentation of lumbar spine vertebrae to identify the deformities using deep learning models. Sensors 2022, 22, 1547.
  96. Tsai, J.Y.; Hung, I.Y.; Guo, Y.L.; Jan, Y.K.; Lin, C.Y.; Shih, T.T.; Chen, B.B.; Lung, C.W. Lumbar disc herniation automatic detection in magnetic resonance imaging based on deep learning. Front. Bioeng. Biotechnol. 2021, 9, 708137.
  97. Yi, W.; Zhao, J.; Tang, W.; Yin, H.; Yu, L.; Wang, Y. Tian, W. Deep learning-based high-accuracy detection for lumbar and cervical degenerative disease on T2-weighted MR images. Eur. Spine J. 2023, 32, pp. 3807–3814,.
  98. Forsberg, D.; Sjöblom, E.; Sunshine, J.L. Detection and labeling of vertebrae in MR images using deep learning with clinical annotations as training data. J. Digit. Imaging 2017, 30, 406–412,.
  99. Zeybel, M.; Akgul, Y.S. Localization and Identification of Lumbar Intervertebral Discs on Spine MR Images with Faster RCNN Based Shortest Path Algorithm. In Proceedings of Annual Conference on Medical Image Understanding and Analysis, Oxford, UK, 15–17 July 2020; pp. 143–154.
  100. Li, H.; Luo, H.; Huan, W.; Shi, Z.; Yan, C.; Wang, L.; Mu, Y. Liu, Y. Automatic lumbar spinal MRI image segmentation with a multi-scale attention network. Neural Comput. Appl. 2021, 33, 11589–11602.
  101. Zheng, H.D.; Sun, Y.L.; Kong, D.W.; Yin, M.-C.; Chen, J.; Lin, Y.-P.; Ma, X.-F.; Wang, H.-S.; Yuan, G.-J.; Yao, M,; et al. Deep learning-based high-accuracy quantitation for lumbar intervertebral disc degeneration from MRI. Nat. Commun. 2022, 13, 841.
  102. Chen, T.; Su, Z.-h.; Liu, Z.; Wang, M.; Cui, Z.-F; Zhao, L.; Yang, L.-J.; Zhang, W.-C.; Liu, X.; Liu, J.; et al. Automated Magnetic Resonance Image Segmentation of Spinal Structures at the L4‐5 Level with Deep Learning: 3D Reconstruction of Lumbar Intervertebral Foramen. Orthop. Surg. 2022, 14, 2256–2264.
  103. Huang, J.; Shen, H.; Wu, J.; Hu, X.; Zhu, Z.; Lv, X.; Liu, Y.; Wang, Y. Spine Explorer: a deep learning based fully automated program for efficient and reliable quantifications of the vertebrae and discs on sagittal lumbar spine MR images. Spine J. 2020, 20, 590–599.
  104. Lu, J.-T.; Pedemonte, S.; Bizzo, B.; Doyle, S.; Andriole, K.P.; Michalski, M.H.; Gonzalez, B.G.; Pomerantz, S.R. Deep spine: automated lumbar vertebral segmentation, disc-level designation, and spinal stenosis grading using deep learning. In Proceedings of Machine Learning for Healthcare Conference, Stanford, CA, USA, 16–18 August 2018; pp. 403–419.
  105. Mbarki, W.; Bouchouicha, M.; Frizzi, S.; Tshibasu, F.; Farhat, L.B.; Sayadi, M. Lumbar spine discs classification based on deep convolutional neural networks using axial view MRI. Interdiscip. Neurosurg. 2020, 22, 100837.
  106. Zhou, J.; Damasceno, P.F.; Chachad, R.; Cheung, J.R.; Ballatori, A.; Lotz, J.C.; Lazar, A.A.; Link, T.M.; Fields, A.J.; Krug, R. Automatic vertebral body segmentation based on deep learning of Dixon images for bone marrow fat fraction quantification. Front. Endocrinol. 2020, 11, 612.
  107. Liu, Z.; Su, Z.; Wang, M.; Chen, T.; Cui, Z.; Chen, X.; Li, S.; Feng, Q.; Pang, S.; Lu, H. Computerized characterization of spinal structures on MRI and clinical significance of 3D reconstruction of lumbosacral intervertebral foramen. Pain Phys. 2022, 25, E27.
  108. Chazen, J.L.; Tan, E.T.; Fiore, J.; Nguyen, J.T.; Sun, S.; Sneag, D.B. Rapid lumbar MRI protocol using 3D imaging and deep learning reconstruction. Skelet. Radiol. 2023, 52, 1331–1338.
  109. Fujiwara, M.; Kashiwagi, N.; Matsuo, C.; Watanabe, H.; Kassai, Y.; Nakamoto, A.; Tomiyama, N. Ultrafast lumbar spine MRI protocol using deep learning–based reconstruction: Diagnostic equivalence to a conventional protocol. Skelet. Radiol. 2023, 52, 233–241.
  110. Han, M.; Bahroos, E.; Hess, M.E.; Chin, C.T.; Gao, K.T.; Shin, D.D.; Villanueva-Meyer, J.E.; Link, T.M.; Pedoia, V.; Majumdar, S. Technology and Tool Development for BACPAC: Qualitative and Quantitative Analysis of Accelerated Lumbar Spine MRI with Deep-Learning Based Image Reconstruction at 3T. Pain Med. 2023, 24, S149–S159.
  111. Zerunian, M.; Pucciarelli, F.; Caruso, D.; De Santis, D.; Polici, M.; Masci, B.; Nacci, I.; Del Gaudio, A.; Argento, G.; Redler, A.; et al. Fast high-quality MRI protocol of the lumbar spine with deep learning-based algorithm: an image quality and scanning time comparison with standard protocol. Skelet. Radiol. 2024, 53, 151–159.
  112. Gao, F.; Wu, M. Deep learning-based denoised MRI images for correlation analysis between lumbar facet joint and lumbar disc herniation in spine surgery. J. Healthc. Eng. vol. 2021, 2021, 9687591.
  113. Sun, S.; Tan, E.T.; Mintz, D.N.; Sahr, M.; Endo, Y.; Nguyen, J.; Lebel, R.M.; Carrino, J.A.; Sneag, D.B. Evaluation of deep learning reconstructed high-resolution 3D lumbar spine MRI. Eur. Radiol. 2022, 32, 6167–6177.
  114. Debelee, G.; Schwenker, F.; Ibenthal, A.; Yohannes, D. Survey of deep learning in breast cancer image analysis. Evol. Syst. 2020, 11, 143–163.
  115. Chen, C.; Qin, C.; Qiu, H.; Tarroni, G.; Duan, J.; Bai, W.; Rueckert, D. Deep learning for cardiac image segmentation: a review. Front. Cardiovasc. Med. 2020, 7, 25.
  116. Bayasi, N.; Hamarneh, G.; Garbi, R. BoosterNet: improving domain generalization of deep neural nets using culpability-ranked features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, 18–24 June 2022, 538–548.
  117. Han, X.; Zhang, Z.; Ding, N.; Gu, Y.; Liu, X.; Huo, Y.; Qiu, J.; Yao, Y.; Zhang, A.; Zhang, L. Pre-trained models: Past, present and future. AI Open 2021, 2, 225–250.
  118. Howard, A.G.; Zhu, M.; Chen, B.; Kalenichenko, D.; Wang, W.; Weyand, T.; Andreetto, M.; Adam, H. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv 2017, arXiv:1704.04861.