Skip to main content

Advertisement

Springer Nature Link
Log in

Statistical and individual characteristics-based reconstruction for craniomaxillofacial surgery

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

Abstract

Purpose

In craniomaxillofacial (CMF) surgery planning, a preoperative reconstruction of the CMF reference model is crucial for surgical restoration, especially the reconstruction of bilateral defects. Current reconstruction algorithms mainly generate reference models from the image analysis aspect, however, clinical indicators of the CMF reference model mostly consider the distribution of anatomical landmarks. Generating a reference model with optimal clinical evaluation helps promote the feasibility of an algorithm.

Methods

We first build a dataset with 100 normal skull models and then calculate a statistical shape model (SSM) and the distribution of normal cephalometric values, which indicate the statistical features of a population. To further generate personalized reference models, we apply non-rigid registration to align the SSM with the defect skull model. An evaluation standard to select the optimal reference model considers both global performance and anatomical evaluation. Moreover, we develop a landmark detection network to improve the automatic level of the algorithm.

Results

The proposed method performs better than methods including Iterative Closest Point and SSM. From a global evaluation aspect, the results show that the RMSE between the reference model and the ground truth is \(1.71 \pm 0.23\) mm, the percentage of vertices with error below 2 mm is \(85 \pm 4\)% and the average faces distance is \(1.38 \pm 0.20\) mm (better than the state-of-the-art method). From the anatomical evaluation aspect, the target registration error between the landmark pairs is \(4.12 \pm 2.27\) mm. In addition, the clinical application confirms that the reference model can meet clinical requirements.

Conclusion

To the best of our knowledge, we propose the first CMF reconstruction method considering the global performance of reconstruction and anatomically local evaluation from clinical experience. Simulated experiments and clinical cases prove the general applicability and strength of the method.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Hupp JR, Tucker MR, Ellis E (2013) Contemporary oral and maxillofacial surgery-e-book. Elsevier health sciences

  2. Sukegawa S, Kanno T, Furuki Y (2018) Application of computer-assisted navigation systems in oral and maxillofacial surgery. Jpn Dental Sci Rev 54(3):139–149

    Article  Google Scholar 

  3. Zhang X, Ye L, Li H, Wang Y, Dilxat D, Liu W, Chen Y, Liu L (2018) Surgical navigation improves reductions accuracy of unilateral complicated zygomaticomaxillary complex fractures: a randomized controlled trial. Sci Rep 8(1):1–9

    Google Scholar 

  4. Lapeer R, Prager R (2000) 3D shape recovery of a newborn skull using thin-plate splines. Comput Med Imaging Graph 24(3):193–204

    Article  CAS  Google Scholar 

  5. Nguyen A, Vanderbeek C, Herford AS, Thakker JS (2019) Use of virtual surgical planning and virtual dataset with intraoperative navigation to guide revision of complex facial fractures: a case report. J Oral Maxillofaci Surg 77 (4):790.e791–790.e717

  6. Ziegler C, Woertche R, Brief J, Hassfeld S (2002) Clinical indications for digital volume tomography in oral and maxillofacial surgery. Dentomaxillofac Radiol 31(2):126–130

    Article  CAS  Google Scholar 

  7. Xia JJ, Gateno J, Teichgraeber JF (2005) Three-dimensional computer-aided surgical simulation for maxillofacial surgery. Atlas Oral Maxillofac Surg Clin North Am 13(1):25–39

    Article  Google Scholar 

  8. Gong X, He Y, He Y, An J-G, Yang Y, Zhang Y (2014) Quantitation of zygomatic complex symmetry using 3-dimensional computed tomography. J Oral Maxillofac Surg 72(10):2053.e2051–2053.e2058

  9. Qiu L, Zhou Z, Guo J, Lv J (2016) An automatic registration algorithm for 3D maxillofacial model. 3D Res 7 (3):1–11

  10. Fuessinger MA, Schwarz S, Cornelius C-P, Metzger MC, Ellis E, Probst F, Semper-Hogg W, Gass M, Schlager S (2018) Planning of skull reconstruction based on a statistical shape model combined with geometric morphometrics. Int J Comput Assist Radiol Surg 13(4):519–529

    Article  Google Scholar 

  11. Cootes TF, Hill A, Taylor CJ, Haslam J (1993) The use of active shape models for locating structures in medical images. Biennial international conference on information processing in medical imaging. Springer, pp 33–47

    Chapter  Google Scholar 

  12. Xiao D, Wang L, Deng H, Thung K-H, Zhu J, Yuan P, Rodrigues YL, Perez L, Crecelius CE, Gateno J (2019) Estimating reference bony shape model for personalized surgical reconstruction of posttraumatic facial defects. International conference on medical image computing and computer-assisted intervention. Springer, pp 327–335

    Google Scholar 

  13. Meyer-Marcotty P, Alpers GW, Gerdes AB, Stellzig-Eisenhauer A (2010) Impact of facial asymmetry in visual perception: a 3-dimensional data analysis. Am J Ortho Dentofac Orthoped 137(2):168.e161–168.e168

  14. Yao B, He Y, Jie B, Wang J, An J, Guo C, Zhang Y (2019) Reconstruction of bilateral post-traumatic midfacial defects assisted by three-dimensional craniomaxillofacial data in normal Chinese people—a preliminary study. J Oral Maxillofac Surg 77(11):2302.e2301–2302.e2313

  15. Cheung LK, Chan YM, Jayaratne YS, Lo J (2011) Three-dimensional cephalometric norms of Chinese adults in Hong Kong with balanced facial profile. Oral Surg Oral Med Oral Pathol Oral Radiol Endodontol 112(2):e56–e73

    Article  Google Scholar 

  16. Gu Y, McNamara JA Jr, Sigler LM, Baccetti T (2011) Comparison of craniofacial characteristics of typical Chinese and Caucasian young adults. Eur J Ortho 33(2):205–211

    Article  Google Scholar 

  17. Cavalcanti MG, Haller JW, Vannier MW (1999) Three-dimensional computed tomography landmark measurement in craniofacial surgical planning: experimental validation in vitro. J Oral Maxillofac Surg 57(6):690–694

    Article  CAS  Google Scholar 

  18. Gateno J, Xia JJ, Teichgraeber JF (2011) New 3-dimensional cephalometric analysis for orthognathic surgery. J Oral Maxillofac Surg 69(3):606–622

    Article  Google Scholar 

  19. Xia J, Gateno J, Teichgraeber J, Yuan P, Chen K-C, Li J, Zhang X, Tang Z, Alfi D (2015) Algorithm for planning a double-jaw orthognathic surgery using a computer-aided surgical simulation (CASS) protocol. Part 1: planning sequence. Int J Oral Maxillofac Surg 44(12):1431–1440

  20. Ma Q, Kobayashi E, Fan B, Nakagawa K, Sakuma I, Masamune K, Suenaga H (2020) Automatic 3D landmarking model using patch‐based deep neural networks for CT image of oral and maxillofacial surgery. Int J Med Robot Comput Assist Surg 16(3):e2093

  21. Wang L, Ren Y, Gao Y, Tang Z, Chen KC, Li J, Shen SG, Yan J, Lee PK, Chow B (2015) Estimating patient-specific and anatomically correct reference model for craniomaxillofacial deformity via sparse representation. Med Phys 42(10):5809–5816

    Article  Google Scholar 

  22. Zhang J, Liu M, Wang L, Chen S, Yuan P, Li J, Shen SG-F, Tang Z, Chen K-C, Xia JJ (2020) Context-guided fully convolutional networks for joint craniomaxillofacial bone segmentation and landmark digitization. Med Image Anal 60:101621

  23. Liu R, Lehman J, Molino P, Such FP, Frank E, Sergeev A, Yosinski J (2018) An intriguing failing of convolutional neural networks and the coordconv solution. arXiv preprint arXiv:180703247

  24. Jie B, Han B, Yao B, Zhang Y, Liao H, He Y (2022) Automatic virtual reconstruction of maxillofacial bone defects assisted by ICP (iterative closest point) algorithm and normal people database. Clin Oral Investig 26(2):2005–2014. https://doi.org/10.1007/s00784-021-04181-3

    Article  PubMed  Google Scholar 

  25. Cootes TF, Taylor CJ, Cooper DH, Graham J (1995) Active shape models-their training and application. Comput Vis Image Underst 61(1):38–59

    Article  Google Scholar 

  26. Amberg B, Romdhani S, Vetter T (2007) Optimal step nonrigid ICP algorithms for surface registration. In: 2007 IEEE conference on computer vision and pattern recognition. IEEE, pp 1–8

Download references

Acknowledgments

The authors acknowledge support from National Natural Science Foundation of China (82027807, 81901844), and Beijing Municipal Natural Science Foundation (7212202, L192013).

Funding

National Natural Science Foundation of China, 82027807, Hongen Liao, 81901844, Longfei Ma, Natural Science Foundation of Beijing Municipality, 7212202, Hongen Liao, Beijing Municipal Natural Science Foundation, L192013, Longfei Ma.

Author information

Author notes

  1. Boxuan Han and Bimeng Jie have contributed equally to this work. All authors read and approved the final manuscript.

Authors and Affiliations

  1. Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China

    Boxuan Han, Lei Zhou, Tianqi Huang, Ruiyang Li, Longfei Ma, Xinran Zhang & Hongen Liao

  2. Department of Oral and Maxillofacial Surgery; National Engineering Laboratory for Digital and Material Technology of Stomatology; Beijing Key Laboratory of Digital Stomatology; National Clinical Research Center for Oral Diseases, Peking University School and Hospital of Stomatology, Beijing, 100081, China

    Bimeng Jie, Yi Zhang & Yang He

Authors
  1. Boxuan Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bimeng Jie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lei Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tianqi Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ruiyang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Longfei Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xinran Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yang He
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Hongen Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Yang He or Hongen Liao.

Ethics declarations

Conflict of interest

The authors have no conflicts of interest to declare that are relevant to the content of this article.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Han, B., Jie, B., Zhou, L. et al. Statistical and individual characteristics-based reconstruction for craniomaxillofacial surgery. Int J CARS 17, 1155–1165 (2022). https://doi.org/10.1007/s11548-022-02626-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-022-02626-y

Keywords