Skip to main content

Advertisement

SpringerLink
Log in

Landmark-guided diffeomorphic demons algorithm and its application to automatic segmentation of the whole spine and pelvis in CT images

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

Abstract

Purpose

A fully automatic multiatlas-based method for segmentation of the spine and pelvis in a torso CT volume is proposed. A novel landmark-guided diffeomorphic demons algorithm is used to register a given CT image to multiple atlas volumes. This algorithm can utilize both grayscale image information and given landmark coordinate information optimally.

Methods

The segmentation has four steps. Firstly, 170 bony landmarks are detected in the given volume. Using these landmark positions, an atlas selection procedure is performed to reduce the computational cost of the following registration. Then the chosen atlas volumes are registered to the given CT image. Finally, voxelwise label voting is performed to determine the final segmentation result.

Results

The proposed method was evaluated using 50 torso CT datasets as well as the public SpineWeb dataset. As a result, a mean distance error of \(0.59\pm 0.14\hbox { mm}\) and a mean Dice coefficient of \(0.90\pm 0.02\) were achieved for the whole spine and the pelvic bones, which are competitive with other state-of-the-art methods.

Conclusion

From the experimental results, the usefulness of the proposed segmentation method was validated.

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  1. Yao J, O’Connor SD, Summers R (2006) Computer aided lytic bone metastasis detection using regular CT images. Reinhardt JM, Pluim JPW (eds) Proc. SPIE 6144, Medical Imaging 2006: Image Processing, vol. 6144. San Diego, CA, pp 614459. doi:10.1117/12.652288

  2. Létourneau D, Kaus M, Wong R, Vloet A, Fitzpatrick DA, Gospodarowicz M, Jaffray DA (2008) Semiautomatic vertebrae visualization, detection, and identification for online palliative radiotherapy of bone metastases of the spine). Med Phys 35(1):367–376

    Article  PubMed  Google Scholar 

  3. Ferrari V, Parchi P, Condino S, Carbone M, Baluganti A, Ferrari M, Mosca F, Lisanti M (2013) An optimal design for patient-specific templates for pedicle spine screws placement. Int J Med Robot Comput Assist Surg 9(3):298–304

    Article  CAS  Google Scholar 

  4. Pereañez M, Lekadir K, Hoogendoorn C, Castro-Mateos I, Frangi A (2015) Detailed vertebral segmentation using part-based decomposition and conditional shape models. In: Yao J, Glocker B, Klinder T, Li S (eds) Recent advances in computational methods and clinical applications for spine imaging. Springer, Switzerland, pp 95–103

  5. Kadoury S, Labelle H, Paragios N (2013) Spine segmentation in medical images using manifold embeddings and higher-order MRFs. IEEE Trans Med Imaging 32(7):1227–1238

    Article  PubMed  Google Scholar 

  6. 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

    Article  PubMed  Google Scholar 

  7. 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

    Article  PubMed  Google Scholar 

  8. Castro-Mateos I, Pozo JM, Pereanez 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  PubMed  Google Scholar 

  9. Korez R, Ibragimov B, Likar B, Pernus 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

    Article  PubMed  Google Scholar 

  10. Iglesias JE, Sabuncu MR (2015) Multi-atlas segmentation of biomedical images: a survey. Med Image Anal 24(1):205–219

    Article  PubMed  PubMed Central  Google Scholar 

  11. Aljabar P, Heckemann RA, Hammers A, Hajnal JV, Rueckert D (2009) Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy. Neuroimage 46(3):726–738

    Article  CAS  PubMed  Google Scholar 

  12. van Rikxoort E, Arzhaeva Y, van Ginneken B (2007) Automatic segmentation of the liver in computed tomography scans with voxel classification and atlas matching. In: Proceedings of the MICCAI workshop, 3D segmentation in the clinic: a grand challenge, 2007, pp 101–108

  13. Rohlfing T, Brandt R, Menzel R, Maurer CR Jr (2004) Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. NeuroImage 21(4):1428–1442. doi:10.1016/j.neuroimage.2003.11.010

    Article  PubMed  Google Scholar 

  14. Heckemann RA, Hajnal JV, Aljabar P, Rueckert D, Hammers A (2006) Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. NeuroImage 33(1):115–126. doi:10.1016/j.neuroimage.2006.05.061

    Article  PubMed  Google Scholar 

  15. Sanroma G, Wu G, Gao Y, Shen D (2014) Learning to rank atlases for multiple-atlas segmentation. IEEE Trans Med Imaging 33:1939–1953

    Article  PubMed  PubMed Central  Google Scholar 

  16. McIntosh C, Purdie TG (2016) Contextual atlas regression forests: multiple-atlas-based automated dose prediction in radiation therapy. IEEE Trans Med Imaging 35:1000–1012

    Article  PubMed  Google Scholar 

  17. Konukoglu E, Glocker B, Zikic D, Criminisi A (2012) Neighbourhood approximation forests. In: Ayache N, Delingette H, Golland P, Mori K (eds) Medical image computing and computer-assisted intervention—MICCAI 2012: 15th international conference, Nice, France, October 1–5, 2012, proceedings, part III. Springer, Berlin Heidelberg, pp 75–82

  18. Thirion J-P (1998) Image matching as a diffusion process: an analogy with Maxwell’s demons. Med Image Anal 2(3):243–260

    Article  CAS  PubMed  Google Scholar 

  19. Vercauteren T, Pennec X, Perchant A, Ayache N (2008) Symmetric log-domain diffeomorphic registration: a demons-based approach. In: Metaxas D, Axel L, Fichtinger G, Székely G (eds) Medical image computing and computer-assisted intervention–MICCAI 2008. Springer, Heidelberg, pp 754–761

  20. Forsberg D (2014) Atlas-based segmentation of the thoracic and lumbar vertebrae. In: Yao J, Glocker B, Klinder T, Li S (eds) Recent advances in computational methods and clinical applications for spine imaging. Springer International Publishing, Cham, pp 215–220

    Google Scholar 

  21. Wang Z, Zhen X, Tay K, Osman S, Romano W, Li S (2015) Regression segmentation for spinal images. IEEE Trans Med Imaging 34(8):1640–1648

    Article  PubMed  Google Scholar 

  22. 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  PubMed  Google Scholar 

  23. Kurtek S, Srivastava A, Klassen E, Laga H (2013) Landmark-guided elastic shape analysis of spherically-parameterized surfaces. Comput Graph Forum 32:429–438

    Article  Google Scholar 

  24. Kearney V, Chen S, Gu X, Chiu T, Liu H, Jiang L, Wang J, Yordy J, Nedzi L, Mao W (2015) Automated landmark-guided deformable image registration. Phys Med Biol 60:101

    Article  PubMed  Google Scholar 

  25. Xie Q, Kurtek S, Klassen E, Christensen GE, Srivastava A (2014) Metric-based pairwise and multiple image registration. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T (eds) Computer vision—ECCV 2014: 13th European conference, Zurich, Switzerland, September 6–12, 2014, proceedings, part II. Springer International Publishing, Cham, pp 236–250

  26. Lam KC, Gu X, Lui LM (2015) Landmark constrained genus-one surface Teichmüller map applied to surface registration in medical imaging. Med Image Anal 25:45–55

    Article  PubMed  Google Scholar 

  27. Nemoto M, Masutani Y, Hanaoka S, Nomura Y, Yoshikawa T, Hayashi N, Yoshioka N, Ohtomo K (2011) A unified framework for concurrent detection of anatomical landmarks for medical image understanding. In: SPIE medical imaging 2011. 7962, 14 Mar 2011. pp 79623E-79623E-79613. doi:10.1117/12.878327

  28. Hanaoka S, Shimizu A, Nemoto M, Nomura Y, Miki S, Yoshikawa T, Hayashi N, Ohtomo K, Masutani Y (2017) Automatic detection of over 100 anatomical landmarks in medical CT images: a framework with independent detectors and combinatorial optimization. Med Image Anal 35:192–214. doi:10.1016/j.media.2016.04.001

  29. Vercauteren T, Pennec X, Perchant A, Ayache N (2007) Non-parametric diffeomorphic image registration with the demons algorithm. In: Ayache N, Ourselin S, Maeder A (eds) Medical image computing and computer-assisted intervention–MICCAI 2007. Springer, Heidelberg, pp 319–326

  30. Seitel A, Rasoulian A, Rohling R, Abolmaesumi P (2015) Lumbar and thoracic spine segmentation using a statistical multi-object shape + pose model. In: Yao J, Glocker B, Klinder T, Li S (eds) Recent advances in computational methods and clinical applications for spine imaging. Springer International Publishing, Cham, pp 221–225

    Chapter  Google Scholar 

  31. Hammernik K, Ebner T, Stern D, Urschler M, Pock T (2015) Vertebrae segmentation in 3D CT images based on a variational framework. In: Yao J, Glocker B, Klinder T, Li S (eds) Recent advances in computational methods and clinical applications for spine imaging. Springer International Publishing, Cham, pp 227–233

    Chapter  Google Scholar 

  32. Korez R, Ibragimov B, Likar B, Pernuš F, Vrtovec T (2015) Interpolation-based shape-constrained deformable model approach for segmentation of vertebrae from ct spine images. In: Yao J, Glocker B, Klinder T, Li S (eds) Recent advances in computational methods and clinical applications for spine imaging. Springer International Publishing, Cham, pp 235–240

    Chapter  Google Scholar 

  33. Castro-Mateos I, Pozo JM, Lazary A, Frangi A (2015) 3D vertebra segmentation by feature selection active shape model. In: Yao J, Glocker B, Klinder T, Li S (eds) Recent advances in computational methods and clinical applications for spine imaging. Springer International Publishing, Cham, pp 241–245

    Chapter  Google Scholar 

  34. Knutsson H, Andersson M (2005) Morphons: segmentation using elastic canvas and paint on priors. In: IEEE international conference on image processing 2005, 11–14 Sept. 2005. pp II-1226–II-1229. doi:10.1109/ICIP.2005.1530283

  35. Paik NC, Lim CS, Jang HS (2013) Numeric and morphological verification of lumbosacral segments in 8280 consecutive patients. Spine 38(10):E573–578. doi:10.1097/BRS.0b013e31828b7195

    Article  PubMed  Google Scholar 

  36. Hanaoka S, Masutani Y, Nemoto M, Nomura Y, Yoshikawa T, Hayashi N, Yoshioka N, Ohtomo K (2011) Probabilistic modeling of landmark distances and structure for anomaly-proof landmark detection. In: Proceedings of the third international workshop on mathematical foundations of computational anatomy, 2011, pp 159–169

Download references

Acknowledgements

This work was supported in part by JSPS Grant-in-Aid for Scientific Research KAKENHI Grant Numbers 15H01108 and 15K19775.

Author information

Authors and Affiliations

  1. Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan

    Shouhei Hanaoka & Kuni Ohtomo

  2. Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei-shi, Tokyo, Japan

    Shouhei Hanaoka & Akinobu Shimizu

  3. Department of Biomedical Information Sciences, Hiroshima City University, Hiroshima, Japan

    Yoshitaka Masutani

  4. Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo, Japan

    Mitsutaka Nemoto, Yukihiro Nomura, Soichiro Miki, Takeharu Yoshikawa & Naoto Hayashi

Authors
  1. Shouhei Hanaoka
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yoshitaka Masutani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mitsutaka Nemoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yukihiro Nomura
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Soichiro Miki
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Takeharu Yoshikawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Naoto Hayashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Kuni Ohtomo
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Akinobu Shimizu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shouhei Hanaoka.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standard

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1975 Declaration of Helsinki, as revised in 2008(5). For this type of study, formal consent is not required.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hanaoka, S., Masutani, Y., Nemoto, M. et al. Landmark-guided diffeomorphic demons algorithm and its application to automatic segmentation of the whole spine and pelvis in CT images. Int J CARS 12, 413–430 (2017). https://doi.org/10.1007/s11548-016-1507-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-016-1507-z

Keywords

Search

Navigation