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The lumbar region localization using bone anatomy feature graphs

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Abstract

The automatic localization of the lumbar region is essential for the diagnosis of lumbar diseases, the study of lumbar morphology, and the surgical planning. Although the existing researches have made great progress, it still faces several challenges. First, the various lumbar diseases and pathologies cause different abnormalities in the lumbar shape and appearance. Second, the numbers of lumbar vertebrae are irregular (some people have an additional vertebra L6). To tackle these challenges, we propose a novel lumbar region localization method based on bone anatomy feature graphs. Specifically, a feature graph (called LS) considering the anatomy of the sacrum and the lumbar vertebra is proposed to locate the inferior boundary of L5 or L6. A feature graph (called TL) considering the anatomy of the thoracic vertebra and the lumbar vertebra is proposed to locate the superior boundary of L1. Extensive experimental analysis is performed on a public available dataset xVertSeg and a private dataset which contains 197 CT scans. The localization results show that the proposed method is robust and can be applied to normal scans, scoliosis scans, deformity scans, hyperosteogeny scans, 6 lumbar vertebrae scans and lumbar implant scans. The Dice and Jaccard coefficients are 98.09 ± 0.84% and 96.27 ± 1.62% respectively.

Lumbar Region Localization Framework

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Availability of data and materials

The private dataset used and/or analysed during the current study are available from the corresponding author on reasonable request. The public xVertSeg dataset can be obtained from http://lit.fe.uni-lj.si/xVertSeg/.

References

  1. Clark S, Horton R (2018) Low back pain: a major global challenge. Lancet 391(10137):2302. https://doi.org/10.1016/S0140-6736(18)30725-6, http://www.sciencedirect.com/science/article/pii/S0140673618307256

    Article  Google Scholar 

  2. Hartvigsen J, Hancock MJ, Kongsted A, Louw Q, Ferreira ML, Genevay S, Hoy D, Karppinen J, Pransky G, Sieper J, Smeets RJ, Underwood M, Buchbinder R, Hartvigsen J, Cherkin D, Foster NE, Maher CG, Underwood M, van Tulder M, Anema JR, Chou R, Cohen SP, Menezes Costa L, Croft P, Ferreira M, Ferreira PH, Fritz JM, Genevay S, Gross DP, Hancock MJ, Hoy D, Karppinen J, Koes BW, Kongsted A, Louw Q, Öberg B, Peul WC, Pransky G, Schoene M, Sieper J, Smeets RJ, Turner JA, Woolf A (2018) What low back pain is and why we need to pay attention. Lancet 391(10137):2356–2367. https://doi.org/10.1016/S0140-6736(18)30480-X

    Article  Google Scholar 

  3. Langworthy JR (1993) Evaluation of impairment related to low back pain. J Med Syst 17:253–256. https://doi.org/10.1007/BF00996954

    Article  CAS  Google Scholar 

  4. Suzani A (2014) Automatic vertebrae localization, identification and segmentation using deep learning and statistical models. PhD thesis, University of British Columbia. https://doi.org/10.14288/1.0166073

  5. Knez D, Likar B, Pernuš F, Vrtovec T (2016) Computer-assisted screw size and insertion trajectory planning for pedicle screw placement surgery. IEEE Trans Med Imaging 35(6):1420–1430. https://doi.org/10.1109/TMI.2016.2514530

    Article  Google Scholar 

  6. Major D, Hladvka J, Schulze F, Bühler K (2013) Automated landmarking and labeling of fully and partially scanned spinal columns in ct images. Med Image Anal 17(8):1151–1163. https://doi.org/10.1016/j.media.2013.07.005, http://www.sciencedirect.com/science/article/pii/S1361841513001126

    Article  Google Scholar 

  7. Huang S, Chu Y, Lai S, Novak CL (2009) Learning-based vertebra detection and iterative normalized-cut segmentation for spinal mri. IEEE Trans Med Imaging 28(10):1595–1605. https://doi.org/10.1109/TMI.2009.2023362

    Article  Google Scholar 

  8. Reaungamornrat S, De Silva T, Uneri A, Vogt S, Kleinszig G, Khanna AJ, Wolinsky J, Prince JL, Siewerdsen JH (2016) Mind demons: Symmetric diffeomorphic deformable registration of mr and ct for image-guided spine surgery. IEEE Trans Med Imaging 35(11):2413–2424. https://doi.org/10.1109/TMI.2016.2576360

    Article  Google Scholar 

  9. Koo TK, Kwok WE (2016) Hierarchical ct to ultrasound registration of the lumbar spine: A comparison with other registration methods. Ann Biomed Eng 44:2887–2900. https://doi.org/10.1007/s10439-016-1599-1

    Article  Google Scholar 

  10. Gueziri HE, Drouin S, Yan CXB, Collins DL (2019) Toward real-time rigid registration of intra-operative ultrasound with preoperative ct images for lumbar spinal fusion surgery. Int J Comput Assist Radiol Surg 14:1933–1943. https://doi.org/10.1007/s11548-019-02020-1

    Article  Google Scholar 

  11. Cai Y, Osman S, Sharma M, Landis M, Li S (2015) Multi-modality vertebra recognition in arbitrary views using 3d deformable hierarchical model. IEEE Trans Med Imaging 34(8):1676–1693. https://doi.org/10.1109/TMI.2015.2392054

    Article  Google Scholar 

  12. Castro-Mateos I, Pozo JM, Pereañez M, Lekadir K, Lazary A, Frangi AF (2015) Statistical interspace models (sims): Application to robust 3d spine segmentation. IEEE Trans Med Imaging 34 (8):1663–1675. https://doi.org/10.1109/TMI.2015.2443912

    Article  Google Scholar 

  13. Korez R, Ibragimov B, Likar B, Pernuš F, Vrtovec T (2015) A framework for automated spine and vertebrae interpolation-based detection and model-based segmentation. IEEE Trans Med Imaging 34 (8):1649–1662. https://doi.org/10.1109/TMI.2015.2389334

    Article  Google Scholar 

  14. Klinder T, Ostermann J, Ehm M, Franz A, Kneser R, Lorenz C (2009) Automated model-based vertebra detection, identification, and segmentation in ct images. Med Image Anal 13(3):471–482. https://doi.org/10.1016/j.media.2009.02.004

    Article  Google Scholar 

  15. Rasoulian A, Rohling R, Abolmaesumi P (2013) Lumbar spine segmentation using a statistical multi-vertebrae anatomical shape+pose model. IEEE Trans Med Imaging 32(10):1890–1900. https://doi.org/10.1109/TMI.2013.2268424

    Article  Google Scholar 

  16. Yang Y, Wang J, Xu C (2019) Intervertebral disc segmentation and diagnostic application based on wavelet denoising and aam model in human spine image. J Med Syst 43:275. https://doi.org/10.1007/s10916-019-1357-7

    Article  Google Scholar 

  17. Ibragimov B, Likar B, Pernuš F, Vrtovec T (2014) Shape representation for efficient landmark-based segmentation in 3-d. IEEE Trans Med Imaging 33(4):861–874. https://doi.org/10.1109/TMI.2013.2296976

    Article  Google Scholar 

  18. Pedro PS, Blaya F, D’Amato R, Juanes JA, Morales LTG, Montes JAR (2019) Geometric model for the postural characterization in the sagital plane of lumbar raquis. J Med Syst 43:130. https://doi.org/10.1007/s10916-019-1249-x

    Article  Google Scholar 

  19. Chengwen C, Belavy DL, Gabriele A, Martin B, Dieter F, Guoyan Z, Dzung P (2015) Fully automatic localization and segmentation of 3d vertebral bodies from ct/mr images via a learning-based method. PloS ONE 10(11):e0143327

    Article  Google Scholar 

  20. Glocker B, Feulner J, Criminisi A, Haynor DR, Konukoglu E (2012) Automatic localization and identification of vertebrae in arbitrary field-of-view ct scans. In: Ayache N, Delingette H, Golland P, Mori K (eds) Medical image computing and computer-assisted intervention – MICCAI 2012. Springer Berlin Heidelberg, Berlin, pp 590–598

  21. Glocker B, Zikic D, Konukoglu E, Haynor DR, Criminisi A (2013) Vertebrae localization in pathological spine ct via dense classification from sparse annotations. In: Mori K, Sakuma I, Sato Y, Barillot C, Navab N (eds) Medical image computing and computer-assisted intervention – MICCAI 2013. Springer Berlin Heidelberg, Berlin, pp 262–270

  22. Oktay AB, Akgul YS (2013) Simultaneous localization of lumbar vertebrae and intervertebral discs with svm-based mrf. IEEE Trans Biomed Eng 60(9):2375–2383. https://doi.org/10.1109/TBME.2013.2256460

    Article  Google Scholar 

  23. Lootus M, Kadir T, Zisserman A (2014) Vertebrae detection and labelling in lumbar mr images. In: Yao J, Klinder T, Li S (eds) Computational methods and clinical applications for spine imaging. Springer International Publishing, Cham, pp 219– 230

  24. Sekuboyina A, Valentinitsch A, Kirschke J, Menze B (2017) A localisation-segmentation approach for multi-label annotation of lumbar vertebrae using deep nets. arXiv:1703.04347

  25. Janssens R, Zeng G, Zheng G (2018) Fully automatic segmentation of lumbar vertebrae from ct images using cascaded 3d fully convolutional networks. In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018). https://doi.org/10.1109/ISBI.2018.8363715, pp 893–897

  26. Lessmann N, van Ginneken B, de Jong PA, Išgum I (2019) Iterative fully convolutional neural networks for automatic vertebra segmentation and identification. Med Image Anal 53:142–155. https://doi.org/10.1016/j.media.2019.02.005

    Article  Google Scholar 

  27. Chen H, Shen C, Qin J, Ni D, Shi L, Cheng JCY, Heng PA (2015) Automatic localization and identification of vertebrae in spine ct via a joint learning model with deep neural networks. In: Navab N, Hornegger J, Wells W M, Frangi A (eds) Medical image computing and computer-assisted intervention – MICCAI 2015. Springer International Publishing, Cham, pp 515– 522

  28. Pereañez M, Lekadir K, Castro-Mateos I, Pozo JM, Lazáry Frangi AF (2015) Accurate segmentation of vertebral bodies and processes using statistical shape decomposition and conditional models. IEEE Trans Med Imaging 34(8):1627–1639. https://doi.org/10.1109/TMI.2015.2396774

    Article  Google Scholar 

  29. Wang X, Zhai S, Niu Y (2019) Automatic vertebrae localization and identification by combining deep ssae contextual features and structured regression forest. J Digit Imaging 32:336–348. https://doi.org/10.1007/s10278-018-0140-5

    Article  Google Scholar 

  30. Alomari RS, Corso JJ, Chaudhary V (2011) Labeling of lumbar discs using both pixel- and object-level features with a two-level probabilistic model. IEEE Trans Med Imaging 30(1):1–10. https://doi.org/10.1109/TMI.2010.2047403

    Article  Google Scholar 

  31. Bergen Den GV (1997) Efficient collision detection of complex deformable models using aabb trees. J Graph Tools 2(4):1–13

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank Yuliang Ma for proofreading this manuscript and suggestions.

Funding

The work was supported by the National Natural Science Foundation of China (Grant No. 61971118).

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Authors and Affiliations

  1. Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China

    Shuang Ma, Jinzhu Yang, Qi Sun, Yuliang Yuan & Yan Huang

  2. School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China

    Shuang Ma, Jinzhu Yang, Qi Sun, Yuliang Yuan & Yan Huang

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  1. Shuang Ma
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  2. Jinzhu Yang
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Correspondence to Jinzhu Yang.

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Ma, S., Yang, J., Sun, Q. et al. The lumbar region localization using bone anatomy feature graphs. Med Biol Eng Comput 59, 2419–2432 (2021). https://doi.org/10.1007/s11517-021-02423-w

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