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A convenient and stable vertebrae instance segmentation method for transforaminal endoscopic surgery planning

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Transforaminal endoscopic surgery (TES) is effective for treatment of intervertebral disc-related diseases. To avoid injury to the critical structures, preoperative planning is required to find a safe working channel. Therefore, accurate patient-specific vertebral segmentation is important. The purpose of this work is to develop a convenient, stable and feasible lumbar vertebrae segmentation method for TES planning.

Methods

Based on the chain structure of the spine, an interactive dual-output vertebrae instance segmentation network was designed to segment the specific vertebrae in CT images. First, an initialization locator module was set up to provide an initial locating box. Then the dual-output network was designed to segment two adjacent vertebrae inside the locating box. Finally, iteration was performed until all the expected vertebrae were segmented.

Results

Verification on reconstructed public dataset showed that the vertebral segmentation Dice coefficient was 96.8 ± 1.2% and average surface distance (ASD) was 0.25 ± 0.10 mm. For intervertebral foramen (IVF) region, the Dice coefficient was 96.1 ± 1.5% and ASD was 0.29 ± 0.10 mm. For IVF forming region, the Dice coefficient was 93.4 ± 3.1% and ASD was 0.28 ± 0.13 mm. The evaluation on private dataset showed that more than 90% of the segmentation were suitable for TES planning. For IVF region, the Dice coefficient was 94.4 ± 1.8% and ASD was 0.71 ± 0.49 mm.

Conclusion

This work provides a convenient, stable and feasible segmentation method for lumbar vertebrae, IVF region, and IVF forming region. The segmentation can meet the requirement for TES planning.

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References

  1. Morgenstern R, Morgenstern C (2015) Percutaneous transforaminal lumbar interbody fusion (pTLIF) with a posterolateral approach for the treatment of degenerative disk disease: feasibility and preliminary results. Int J Spine Surg 9:41

    Article  Google Scholar 

  2. Nellensteijn J, Ostelo R, Bartels R, Peul W, van Royen B, van Tulder M (2010) Transforaminal endoscopic surgery for symptomatic lumbar disc herniations: a systematic review of the literature. Eur Spine J 19(2):181–204

    Article  Google Scholar 

  3. Park JW, Nam HS, Cho SK, Jung HJ, Lee BJ, Park Y (2011) Kambin’s triangle approach of lumbar transforaminal epidural injection with spinal stenosis. Ann Rehabil Med 35(6):833–843

    Article  Google Scholar 

  4. Pairaiturkar PP, Sudame OS, Pophate CS (2019) Evaluation of dimensions of Kambin’s triangle to calculate maximum permissible cannula diameter for percutaneous endoscopic lumbar discectomy: a 3-dimensional magnetic resonance imaging based study. J Korean Neurosurg Soc 62(4):414–421

    Article  Google Scholar 

  5. Sekuboyina A, Kukačka J, Kirschke JS, Menze BH, Valentinitsch A (2018) Attention-driven deep learning for pathological spine segmentation. In: Glocker B, Yao J, Vrtovec T, Frangi A, Zheng G (eds), 1 January 2018. Springer, Cham, pp 108–119

  6. Vania M, Argaw DM, Lee D (2017) Automatic spine segmentation using convolutional neural network via redundant generation of class labels for 3D spine modeling. J Comput Des Eng 6(2):224–232

    Google Scholar 

  7. Joutard S, Dorent R, Isaac A, Ourselin S, Vercauteren T, Modat M (2019) Permutohedral attention module for efficient non-local neural networks. In: Shen D, Liu T, Peters TM, Staib LH, Essert C, Zhou S, Yap PT, Khan A (eds) Medical image computing and computer assisted intervention—Miccai 2019, Pt Vi. Lecture notes in computer science, 1 January 2019, vol 11769, pp 393–401

  8. Sekuboyina A, Valentinitsch A, Kirschke JS, Menze BH (2017) A localisation-segmentation approach for multi-label annotation of lumbar vertebrae using deep nets. arXiv e-prints, pp 1703–4347

  9. 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), pp 893–897

  10. Shi D, Pan Y, Liu C, Wang Y, Cui D, Lu Y, Assoc Comp M (2018) Automatic localization and segmentation of vertebral bodies in 3D CT volumes with deep learning. In: ISICDM 2018. Proceedings of the 2nd international symposium on image computing and digital medicine

  11. Lessmann N, Ginneken B, Jong P, Išgum I (2019) Iterative fully convolutional neural networks for automatic vertebra segmentation and identification. Med Image Anal 53:142–155

    Article  Google Scholar 

  12. Chuang C, Lin C, Tsai Y, Lian Z, Xie H, Hsu C, Huang C (2019) Efficient triple output network for vertebral segmentation and identification. IEEE Access 7:117978–117985

    Article  Google Scholar 

  13. Liu P, Lian Z, Lin C, Chuang C, Huang C, Tsai Y (2018) Two staged machine learning network for spine segmentation and recognition. In: 2018 IEEE international symposium on multimedia (ISM)

  14. Yao J, Burns JE, Forsberg D, Seitel A, Rasoulian A, Abolmaesumi P, Hammernik K, Urschler M, Ibragimov B, Korez R, Vrtovec T, Castro-Mateos I, Pozo JM, Frangi AF, Summers RM, Li S (2016) A multi-center milestone study of clinical vertebral CT segmentation. Comput Med Imaging Graph 49:16–28

    Article  Google Scholar 

  15. Huang J, Jian F, Wu H, Li H (2013) An improved level set method for vertebra CT image segmentation. Biomed Eng Online 12:48

    Article  Google Scholar 

  16. Liu X, Yang J, Song S, Cong W, Jiao P, Song H, Ai D, Jiang Y, Wang Y (2018) Sparse intervertebral fence composition for 3D cervical vertebra segmentation. Phys Med Biol 63(11):115010

    Article  Google Scholar 

  17. 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

    Article  Google Scholar 

  18. Isensee F, Jager P, Wasserthal J, Zimmerer D, Petersen J, Kohl S, Schock J, Klein A, RoSs T, Wirkert S, Neher P, Dinkelacker S, Kohler G, Maier-Hein K (2020) batchgenerators—a python framework for data augmentation. https://doi.org/10.5281/zenodo.3632567

  19. Löffler MT, Sekuboyina A, Jacob A, Grau A, Scharr A, El Husseini M, Kallweit M, Zimmer C, Baum T, Kirschke JS (2020) A vertebral segmentation dataset with fracture grading. Radiol Artif Intell 2(4):e190138

    Article  Google Scholar 

  20. Sekuboyina A, Husseini ME, Bayat A, Löffler M, Liebl H, Li H, Tetteh G, Kukačka J, Payer C, Štern D, Urschler M, Chen M, Cheng D, Lessmann N, Hu Y, Wang T, Yang D, Xu D, Ambellan F, Amiranashvili T, Ehlke M, Lamecker H, Lehnert S, Lirio M, de Olaguer NP, Ramm H, Sahu M, Tack A, Zachow S, Jiang T, Ma X, Angerman C, Wang X, Brown K, Wolf M, Kirszenberg A, Puybareau É, Di Chen, Bai Y, Rapazzo BH, Yeah T, Zhang A, Xu S, Hou F, He Z, Zeng C, Xiangshang Z, Liming X, Netherton TJ, Mumme RP, Court LE, Huang Z, He C, Wang L, Ling SH, Huynh LD, Boutry N, Jakubicek R, Chmelik J, Mulay S, Sivaprakasam M, Paetzold JC, Shit S, Ezhov I, Wiestler B, Glocker B, Valentinitsch A, Rempfler M, Menze BH, Kirschke JS (2021) VerSe: a vertebrae labelling and segmentation benchmark for multi-detector CT images. arXiv e-prints: 2001.09193

  21. Sekuboyina A, Rempfler M, Valentinitsch A, Menze BH, Kirschke JS (2020) Labeling vertebrae with two-dimensional reformations of multidetector CT images: an adversarial approach for incorporating prior knowledge of spine anatomy. Radiol Artif Intell 2(2):e190074

    Article  Google Scholar 

  22. Sekuboyina A, Rempfler M, Kukačka J, Tetteh G, Valentinitsch A, Kirschke JS, Menze BH (2018) Btrfly net: vertebrae labelling with energy-based adversarial learning of local spine prior. In: Frangi AF, Schnabel JA, Davatzikos C, Alberola-López C, Fichtinger G (eds), 1 January 2018. Springer, Cham, pp 649–657

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (U20A20389), Beijing Municipal Science & Technology Commission (Z191100007619036, L192046)

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Authors and Affiliations

  1. Department of Biomedical Engineering, School of Medicine, Tsinghua University, Room C249, Beijing, 100084, People’s Republic of China

    Wuke Peng, Liang Li, Libin Liang, Hui Ding & Guangzhi Wang

  2. Department of Orthopedics, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100043, People’s Republic of China

    Lei Zang & Shuo Yuan

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  1. Wuke Peng
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  2. Liang Li
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  7. Guangzhi Wang
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Correspondence to Guangzhi Wang.

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Peng, W., Li, L., Liang, L. et al. A convenient and stable vertebrae instance segmentation method for transforaminal endoscopic surgery planning. Int J CARS 16, 1263–1276 (2021). https://doi.org/10.1007/s11548-021-02429-7

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  • DOI: https://doi.org/10.1007/s11548-021-02429-7

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