Skip to main content
SpringerLink
Log in

Incremental regression of localization context for automatic segmentation of ossified ligamentum flavum from CT data

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Segmentation of ossified ligamentum flavum (OLF) plays a crucial role in developing computer-assisted, image-guided systems for decompressive thoracic laminectomy. Manual segmentation is time-consuming, tedious, and label-intensive. It also suffers from inter- and intra-observer variability. Automatic segmentation is highly desired.

Methods

A two-stage, localization context-aware framework is developed for automatic segmentation of ossified ligamentum flavum. In the first stage, localization heatmaps of OLFs are obtained via incremental regression. In the second stage, the obtained heatmaps are then treated as the localization context for a segmentation U-Net. Our framework can directly map a whole volumetic data to its volume-wise labels.

Results

We designed and conducted comprehensive experiments on datasets of 100 patients to evaluate the performance of the proposed method. Our method achieved an average Dice similarity coefficient of 61.2 ± 7.6%, an average surface distance of 1.1 ± 0.5 mm, and an average positive predictive value of 62.0 ± 12.8%.

Conclusion

To the best knowledge of the authors, this is the first study aiming for automatic segmentation of ossified ligamentum flavum. Results from the comprehensive experiments demonstrate the superior performance of the proposed method over the state-of-the-art methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Hirabayashi S (2017) Ossification of the ligamentum flavum. Spine Surg Relat Res 1(4):158–163

    Article  PubMed  PubMed Central  Google Scholar 

  2. Lang N, Yuan HS, Wang HL, Liao J, Li M, Guo FX, Shi S, Chen ZQ (2013) Epidemiological survey of ossification of the ligamentum flavum in thoracic spine: Ct imaging observation of 993 cases. Eur Spine J 22:857–862

    Article  PubMed  Google Scholar 

  3. Yan C, Tan H-Y, Ji C-L, Yu X-W, Jia H-C, Li F-D, Jiang G-C, Li W-S, Zhou F-F, Ye Z, Sun J-C, Shi J-G (2021) The clinical value of three-dimensional measurement in the diagnosis of thoracic myelopathy caused by ossification of the ligamentum flavum. Quant Imaging Med Surg 11(5):2040

    Article  PubMed  PubMed Central  Google Scholar 

  4. Li X, An B, Gao H, Zhou C, Zhao X, Ma H, Wang B, Yang H, Zhou H, Guo X, Zhu H, Qian J (2020) Surgical results and prognostic factors following percutaneous full endoscopic posterior decompression for thoracic myelopathy caused by ossification of the ligamentum flavum. Sci Rep 10(1):1305

    Article  PubMed  PubMed Central  Google Scholar 

  5. Wang Z-W, Wang Z, Fan X-W, Du P-Y, Sun J-Y, Ding W-Y, Yang D-L (2020) Precise surgical treatment of thoracic ossification of ligamentum flavum assisted by o-arm computer navigation: a retrospective study. World Neurosurgery 143:409–418

    Article  Google Scholar 

  6. Wang Z, Wu R, Xu Y, Liu Y, Chai R, Ma H (2023) A two-stage CNN method for MRI image segmentation of prostate with lesion. Biomed Signal Process Control 82:104610

    Article  Google Scholar 

  7. Wu J, Liu X, Liao Y (2022) Difficulty-aware brain lesion segmentation from MRI scans. Neural Process Lett 54(3):1961–1975

    Article  Google Scholar 

  8. Gros C, De Leener B, Badji A, Maranzano J, Eden D, Dupont SM, Talbott J, Zhuoquiong R, Liu Y, Granberg T, Ouellette R (2019) Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks. Neuroimage 184:901–915

    Article  PubMed  Google Scholar 

  9. Sekuboyina A, Husseini ME, Bayat A, Löffler M, Liebl H, Li H, Tetteh G, Kukačka J, Payer C, Štern D, Urschler M (2021) Verse: a vertebrae labelling and segmentation benchmark for multi-detector CT images. Med Image Anal 73:102166

    Article  PubMed  Google Scholar 

  10. Payer C, Stern D, Bischof H, Urschler M (2020) Coarse to fine vertebrae localization and segmentation with spatial configuration-net and u-net. In: VISIGRAPP (5: VISAPP), pp. 124–133

  11. Cermelli F, Mancini M, Bulo SR, Ricci E, Caputo B (2020) Modeling the background for incremental learning in semantic segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 9233–9242

  12. Patravali J, Jain S, Chilamkurthy S (2018) 2d-3d fully convolutional neural networks for cardiac mr segmentation. In: STACOM 2017, Held in Conjunction with MICCAI 2017, Quebec City, Canada, 10–14 Sept 2017, Springer, Berlin, pp. 130–139

  13. Wang R, Zheng G (2022) CyCMIS: cycle-consistent cross-domain medical image segmentation via diverse image augmentation. Med Image Anal 76:102328

    Article  PubMed  Google Scholar 

  14. Bottou L, Curtis FE, Nocedal J (2018) Optimization methods for large-scale machine learning. SIAM Rev 60:223–311

    Article  Google Scholar 

  15. Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2881–2890

  16. Rezatofighi H, Tsoi N, Gwak J, Sadeghian A, Reid I, Savarese S (2019) Generalized intersection over union: a metric and a loss for bounding box regression. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 658–666

  17. Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3d u-net: learning dense volumetric segmentation from sparse annotation. In: Medical image computing and computer-assisted intervention—MICCAI 2016: 19th international conference, Athens, Greece, October 17–21, 2016, Proceedings, Part II 19. Springer, Berlin, pp 424–432

  18. Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier-Hein KH (2021) NNU-NET: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18(2):203–211

    Article  CAS  PubMed  Google Scholar 

Download references

Funding

This research was partially supported by the National Natural Science Foundation of China (U20A20199).

Author information

Authors and Affiliations

  1. Institute of Medical Robotics, Shanghai Jiao Tong University, Dongchuan Road, Shanghai, 200240, China

    Rong Tao, Xiaoyang Zou, Xiaoru Gao & Guoyan Zheng

  2. Department of Orthopedics, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China

    Xinhua Li & Donghua Hang

  3. Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China

    Zhiyu Wang

  4. Department of Orthopedics, Shanghai 9th People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China

    Xin Zhao

Authors
  1. Rong Tao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoyang Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaoru Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xinhua Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhiyu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xin Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Guoyan Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Donghua Hang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Xin Zhao, Guoyan Zheng or Donghua Hang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethics approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed context was obtained from all individuals included in the study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tao, R., Zou, X., Gao, X. et al. Incremental regression of localization context for automatic segmentation of ossified ligamentum flavum from CT data. Int J CARS (2024). https://doi.org/10.1007/s11548-024-03109-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11548-024-03109-y

Keywords

Search

Navigation