Skip to main content
SpringerLink
Log in

Machine Learning for Craniomaxillofacial Landmark Digitization of 3D Imaging

  • Chapter
  • First Online:
Machine Learning in Dentistry

Abstract

Craniomaxillofacial (CMF) deformities (including congenital and acquired deformities of the head and face) seriously affect patients’ daily lives. For computer-aided diagnosis and treatment planning, cone-beam computed tomography (CBCT) is typically used to scan CMF patients, where anatomical landmarks are digitized to quantitatively assess the CMF anatomy. This chapter presents the latest machine learning methods for CMF landmark digitization of 3D CBCT images. Specifically, four methods for landmark digitization are described, including (1) a multi-atlas-based method, (2) a regression forest-based approach, (3) a segmentation-guided regression forest method, and (4) a deep learning approach. Experimental results demonstrate that machine learning-based methods help boost the digitization performance of anatomical landmarks for CMF patients.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Wang L, Chen KC, Gao Y, Shi F, Liao S, Li G, Shen SG, Yan J, Lee PK, Chow B, et al. Automated bone segmentation from dental cbct images using patch-based sparse representation and convex optimization. Med Phys. 2014;41(4):043503.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Li Z, An L, Zhang J, Wang L, Xia JJ, Shen D. Craniomaxillofacial deformity correction via sparse representation in coherent space. In: International workshop on machine learning in medical imaging: Springer; 2015. p. 69–76.

    Chapter  Google Scholar 

  3. Zhang J, Cain EH, Saha A, Zhu Z, Mazurowski MA. Breast mass detection in mammography and tomosynthesis via fully convolutional network-based heatmap regression. In: Medical imaging 2018: computer-aided diagnosis, vol. 10575: International Society for Optics and Photonics; 2018. p. 1057525.

    Google Scholar 

  4. Zhang J, Saha A, Zhu Z, Mazurowski MA. Hierarchical convolutional neural networks for segmentation of breast tumors in mri with application to radiogenomics. IEEE Trans Med Imaging. 2018;38(2):435–47.

    Article  PubMed  Google Scholar 

  5. Cao X, Yang J, Zhang J, Wang Q, Yap PT, Shen D. Deformable image registration using a cue-aware deep regression network. IEEE Trans Biomed Eng. 2018;65(9):1900–11.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Liu M, Zhang J, Nie D, Yap PT, Shen D. Anatomical landmark based deep feature representation for mr images in brain disease diagnosis. IEEE J Biomed Health Inform. 2018;22(5):1476–85.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Lian C, Liu M, Zhang J, Shen D. Hierarchical fully convolutional network for joint atrophy localization and alzheimer’s disease diagnosis using structural mri. IEEE Trans Pattern Anal Mach Intell. 2018; https://doi.org/10.1109/TPAMI.2018.2889096.

  8. Liu M, Zhang J, Adeli E, Shen D. Landmark-based deep multi-instance learning for brain disease diagnosis. Med Image Anal. 2018;43:157–68.

    Article  PubMed  Google Scholar 

  9. Zhang J, Liu M, An L, Gao Y, Shen D. Alzheimer’s disease diagnosis using landmark-based features from longitudinal structural mr images. IEEE J Biomed Health Inform. 2017;21(6):1607–16.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Liu M, Zhang J, Adeli E, Shen D. Joint classification and regression via deep multi-task multi-channel learning for alzheimer’s disease diagnosis. IEEE Trans Biomed Eng. 2018;66(5):1195–206.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Liu M, Zhang J, Lian C, Shen D. Weakly supervised deep learning for brain disease prognosis using mri and incomplete clinical scores. IEEE transactions on cybernetics. 2019;

    Google Scholar 

  12. Zhang J, Liu M, Wang L, Chen S, Yuan P, Li J, Shen SGF, Tang Z, Chen KC, Xia JJ, et al. Joint craniomaxillofacial bone segmentation and landmark digitization by context-guided fully convolutional networks. In: International conference on medical image computing and computer-assisted intervention: Springer; 2017. p. 720–8.

    Google Scholar 

  13. Zhang J, Gao Y, Wang L, Tang Z, Xia JJ, Shen D. Automatic craniomaxillofacial landmark digitization via segmentation-guided partially-joint regression forest model. In: International conference on medical image computing and computer-assisted intervention: Springer; 2015. p. 661–8.

    Google Scholar 

  14. Donner R, Micuvsik B, Langs G, Bischof H. Sparse mrf appearance models for fast anatomical structure localisation. In: Proc: BMVC; 2007.

    Google Scholar 

  15. Donner R, Langs G, Mivcuvsik B, Bischof H. Generalized sparse mrf appearance models. Image Vis Comput. 2010;28(6):1031–8.

    Article  Google Scholar 

  16. Nowinski WL, Thirunavuukarasuu A. Atlas-assisted localization analysis of functional images. Med Image Anal. 2001;5(3):207–20.

    Article  PubMed  Google Scholar 

  17. Yelnik J, Damier P, Demeret S, Gervais D, Bardinet E, Bejjani BP, Franccois C, Houeto JL, Arnulf I, Dormont D, et al. Localization of stimulating electrodes in patients with parkinson disease by using a three-dimensional atlas-magnetic resonance imaging coregistration method. J Neurosurg. 2003;99(1):89–99.

    Article  PubMed  Google Scholar 

  18. Fenchel M, Thesen S, Schilling A. Automatic labeling of anatomical structures in mr fastview images using a statistical atlas. In: Medical image computing and computer-assisted intervention–MICCAI 2008: Springer; 2008. p. 576–84.

    Chapter  Google Scholar 

  19. Shahidi S, Bahrampour E, Soltanimehr E, Zamani A, Oshagh M, Moattari M, Mehdizadeh A. The accuracy of a designed software for automated localization of craniofacial landmarks on cbct images. BMC Med Imaging. 2014;14(1):32.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Zhang J, Gao Y, Wang L, Tang Z, Xia JJ, Shen D. Automatic craniomaxillofacial landmark digitization via segmentation-guided partially-joint regression forest model and multiscale statistical features. IEEE Trans Biomed Eng. 2015;63(9):1820–9.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Zhang J, Liu M, Shen D. Detecting anatomical landmarks from limited medical imaging data using two-stage task-oriented deep neural networks. IEEE Trans Image Process. 2017;26(10):4753–64.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Zhang J, Gao Y, Gao Y, Munsell B, Shen D. Detecting anatomical landmarks for fast Alzheimer’s disease diagnosis. IEEE Trans Med Imaging. 2016;35(12):2524–33.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Zhan Y, Zhou XS, Peng Z, Krishnan A. Active scheduling of organ detection and segmentation in whole-body medical images. In: Medical image computing and computer-assisted intervention–MICCAI 2008: Springer; 2008. p. 313–21.

    Chapter  Google Scholar 

  24. Criminisi A, Shotton J, Bucciarelli S. Decision forests with long-range spatial context for organ localization in ct volumes. In: Medical image computing and computer-assisted Interventation (MICCAI); 2009. p. 69–80.

    Google Scholar 

  25. Cheng E, Chen J, Yang J, Deng H, Wu Y, Megalooikonomou V, Gable B, Ling H. Automatic dent-landmark detection in 3-d cbct dental volumes. In: Engineering in medicine and biology society, EMBC, 2011: Annual International Conference of the IEEE; 2011. p. 6204–7.

    Google Scholar 

  26. Zhan Y, Dewan M, Harder M, Krishnan A, Zhou XS. Robust automatic knee MR slice positioning through redundant and hierarchical anatomy detection. IEEE Tran on Medical Imaging. 2011;30(12):2087–100.

    Article  Google Scholar 

  27. Criminisi A, Shotton J, Robertson D, Konukoglu E. Regression forests for efficient anatomy detection and localization in ct studies. In: Medical computer vision. Recognition techniques and applications in medical imaging: Springer; 2011. p. 106–17.

    Chapter  Google Scholar 

  28. Cootes TF, Ionita MC, Lindner C, Sauer P. Robust and accurate shape model fitting using random forest regression voting. In: ECCV 2012: Springer; 2012. p. 278–91.

    Google Scholar 

  29. Criminisi A, Robertson D, Konukoglu E, Shotton J, Pathak S, White S, Siddiqui K. Regression forests for efficient anatomy detection and localization in computed tomography scans. Med Image Anal. 2013;17(8):1293–303.

    Article  PubMed  Google Scholar 

  30. Lindner C, Thiagarajah S, Wilkinson JM, Consortium T, Wallis G, Cootes T. Fully automatic segmentation of the proximal femur using random forest regression voting. Medical Imaging, IEEE Transactions on. 2013;32(8):1462–72.

    Article  Google Scholar 

  31. Chu C, Chen C, Wang CW, Huang CT, Li CH, Nolte LP, Zheng G. Fully automatic cephalometric x-ray landmark detection using random forest regression and sparse shape composition. submitted to Automatic Cephalometric X-ray Landmark Detection Challenge. 2014.

    Google Scholar 

  32. Gao Y, Shen D. Context-aware anatomical landmark detection: application to deformable model initialization in prostate ct images. In: Machine learning in medical imaging: Springer; 2014. p. 165–73.

    Chapter  Google Scholar 

  33. Donner R, Menze BH, Bischof H, Langs G. Global localization of 3d anatomical structures by pre-filtered hough forests and discrete optimization. Med Image Anal. 2013;17(8):1304–14.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Chen C, Xie W, Franke J, Grutzner P, Nolte LP, Zheng G. Automatic x-ray landmark detection and shape segmentation via data-driven joint estimation of image displacements. Med Image Anal. 2014;18(3):487–99.

    Article  PubMed  Google Scholar 

  35. Chen, C., Belavy, D., Yu, W., Chu, C., Armbrecht, G., Bansmann, M., Felsenberg, D., Zheng, G.: Localization and segmentation of 3d intervertebral discs in mr images by data driven estimation. (2015).

    Book  Google Scholar 

  36. Zheng Y, Liu D, Georgescu B, Nguyen H, Comaniciu D. 3D deep learning for efficient and robust landmark detection in volumetric data. In: International conference on medical image computing and computer-assisted intervention: Springer; 2015. p. 565–72.

    Google Scholar 

  37. Riegler G, Urschler M, Ruther M, Bischof H, Stern D. Anatomical landmark detection in medical applications driven by synthetic data. In: Proceedings of the IEEE international conference on computer vision workshops; 2015. p. 12–6.

    Google Scholar 

  38. Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R, LeCun Y. Overfeat: integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv. 2013;1312:6229.

    Google Scholar 

  39. Tompson J, Goroshin R, Jain A, LeCun Y, Bregler C. Efficient object localization using convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2015. p. 648–56.

    Google Scholar 

  40. Liang Z, Ding S, Lin L. Unconstrained facial landmark localization with backbone-branches fully-convolutional networks. arXiv preprint arXiv. 2015;1507:03409.

    Google Scholar 

  41. Dai J, Li Y, He K, Sun J. R-FCN: object detection via region-based fully convolutional networks. In: Advances in neural information processing systems; 2016. p. 379–87.

    Google Scholar 

  42. Payer C, Štern D, Bischof H, Urschler M. Regressing heatmaps for multiple landmark localization using CNNs. In: MICCAI: Springer; 2016. p. 230–8.

    Google Scholar 

  43. Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. Fsl Neuroimage. 2012;62(2):782–90.

    Article  PubMed  Google Scholar 

  44. Thirion JP. Image matching as a diffusion process: an analogy with maxwell’s demons. Med Image Anal. 1998;2(3):243–60.

    Article  PubMed  Google Scholar 

  45. Avants BB, Epstein CL, Grossman M, Gee JC. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal. 2008;12(1):26–41.

    Article  PubMed  Google Scholar 

  46. Cao X, Yang J, Zhang J, Nie D, Kim M, Wang Q, Shen D. Deformable image registration based on similarity-steered cnn regression. In: International conference on medical image computing and computer-assisted intervention: Springer; 2017. p. 300–8.

    Google Scholar 

  47. Zhang J. Inverse-consistent deep networks for unsupervised deformable image registration. arXiv preprint arXiv. 2018;1809:03443.

    Google Scholar 

  48. Zhang S, Zhan Y, Dewan M, Huang J, Metaxas DN, Zhou XS. Towards robust and effective shape modeling: sparse shape composition. Med Image Anal. 2012;16(1):265–77.

    Article  PubMed  Google Scholar 

  49. Zhang J, Liang J, Zhao H. Local energy pattern for texture classification using self-adaptive quantization thresholds. Image Processing, IEEE Transactions on. 2013;22(1):31–42.

    Article  Google Scholar 

  50. Pfister T, Charles J, Zisserman A. Flowing convnets for human pose estimation in videos. In: ICCV; 2015. p. 1913–21.

    Google Scholar 

  51. Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: MICCAI: Springer; 2015. p. 234–41.

    Google Scholar 

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

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Tencent AI Lab, Shenzhen, Guangdong, China

    Jun Zhang

  2. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Mingxia Liu, Li Wang, Chunfeng Lian & Dinggang Shen

Authors
  1. Jun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mingxia Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Li Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chunfeng Lian
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dinggang Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Ohio State University, Columbus, OH, USA

    Ching-Chang Ko

  2. School of Biomedical Engineering, ShanghaiTech University, Shanghai, Shanghai, China

    Dinggang Shen

  3. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Li Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Zhang, J., Liu, M., Wang, L., Lian, C., Shen, D. (2021). Machine Learning for Craniomaxillofacial Landmark Digitization of 3D Imaging. In: Ko, CC., Shen, D., Wang, L. (eds) Machine Learning in Dentistry. Springer, Cham. https://doi.org/10.1007/978-3-030-71881-7_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-71881-7_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71880-0

  • Online ISBN: 978-3-030-71881-7

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation