Skip to main content

Advertisement

SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
Book cover

International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2012: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 pp 66–74Cite as

  1. Home
  2. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012
  3. Conference paper
Automatic Detection and Segmentation of Kidneys in 3D CT Images Using Random Forests

Automatic Detection and Segmentation of Kidneys in 3D CT Images Using Random Forests

  • Rémi Cuingnet19,
  • Raphael Prevost19,20,
  • David Lesage19,
  • Laurent D. Cohen20,
  • Benoît Mory19 &
  • …
  • Roberto Ardon19 
  • Conference paper
  • 4962 Accesses

  • 68 Citations

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7512)

Abstract

Kidney segmentation in 3D CT images allows extracting useful information for nephrologists. For practical use in clinical routine, such an algorithm should be fast, automatic and robust to contrast-agent enhancement and fields of view. By combining and refining state-of-the-art techniques (random forests and template deformation), we demonstrate the possibility of building an algorithm that meets these requirements. Kidneys are localized with random forests following a coarse-to-fine strategy. Their initial positions detected with global contextual information are refined with a cascade of local regression forests. A classification forest is then used to obtain a probabilistic segmentation of both kidneys. The final segmentation is performed with an implicit template deformation algorithm driven by these kidney probability maps. Our method has been validated on a highly heterogeneous database of 233 CT scans from 89 patients. 80 % of the kidneys were accurately detected and segmented (Dice coefficient > 0.90) in a few seconds per volume.

Keywords

  • Random Forest
  • Regression Forest
  • Decision Forest
  • Deformation Algorithm
  • Heterogeneous Database

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Download conference paper PDF

References

  1. Spiegel, M., et al.: Segmentation of kidneys using a new active shape model generation technique based on non-rigid image registration. Comput. Med. Imaging Graph. 33(1), 29–39 (2009)

    CrossRef  Google Scholar 

  2. Li, X., Chen, X., Yao, J., Zhang, X., Tian, J.: Renal Cortex Segmentation Using Optimal Surface Search with Novel Graph Construction. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 387–394. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  3. Khalifa, F., Elnakib, A., Beache, G.M., Gimel’farb, G., El-Ghar, M.A., Ouseph, R., Sokhadze, G., Manning, S., McClure, P., El-Baz, A.: 3D Kidney Segmentation from CT Images Using a Level Set Approach Guided by a Novel Stochastic Speed Function. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 587–594. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  4. Tsagaan, B., Shimizu, A., Kobatake, H., Miyakawa, K.: An Automated Segmentation Method of Kidney Using Statistical Information. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002, Part I. LNCS, vol. 2488, pp. 556–563. Springer, Heidelberg (2002)

    CrossRef  Google Scholar 

  5. Mory, B., Somphone, O., Prevost, R., Ardon, R.: Real-Time 3D Image Segmentation by User-Constrained Template Deformation. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 560–567. Springer, Heidelberg (2012)

    Google Scholar 

  6. Fenchel, M., Thesen, S., Schilling, A.: Automatic Labeling of Anatomical Structures in MR FastView Images Using a Statistical Atlas. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 576–584. Springer, Heidelberg (2008)

    CrossRef  Google Scholar 

  7. Isgum, I., et al.: Multi-atlas-based segmentation with local decision fusion: Application to cardiac and aortic segmentation in CT scans. IEEE Trans. Med. Imaging 28(7), 1000–1010 (2009)

    CrossRef  Google Scholar 

  8. Montillo, A., Shotton, J., Winn, J., Iglesias, J.E., Metaxas, D., Criminisi, A.: Entangled Decision Forests and Their Application for Semantic Segmentation of CT Images. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 184–196. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  9. Criminisi, A., et al.: Decision Forests with Long-Range Spatial Context for Organ Localization in CT Volumes. In: MICCAI Workshop PMMIA (2009)

    Google Scholar 

  10. Georgescu, B., et al.: Database-guided segmentation of anatomical structures with complex appearance. In: CVPR, vol. 2, pp. 429–436. IEEE (2005)

    Google Scholar 

  11. Zheng, Y., et al.: Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features. IEEE Trans. Med. Imaging 27(11), 1668–1681 (2008)

    CrossRef  Google Scholar 

  12. Zhou, S., et al.: Image based regression using boosting method. In: ICCV, vol. 1, pp. 541–548. IEEE (2005)

    Google Scholar 

  13. Criminisi, A., Shotton, J., Robertson, D., Konukoglu, E.: Regression Forests for Efficient Anatomy Detection and Localization in CT Studies. In: Menze, B., Langs, G., Tu, Z., Criminisi, A. (eds.) MICCAI 2010. LNCS, vol. 6533, pp. 106–117. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  14. Pauly, O., Glocker, B., Criminisi, A., Mateus, D., Möller, A.M., Nekolla, S., Navab, N.: Fast Multiple Organ Detection and Localization in Whole-Body MR Dixon Sequences. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 239–247. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  15. Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)

    CrossRef  MATH  Google Scholar 

  16. Criminisi, A., et al.: Decision forests for classification, regression, density estimation, manifold learning and semi-supervised learning. Technical report, Microsoft Research (2011)

    Google Scholar 

  17. Dollar, P., et al.: Cascaded pose regression. In: CVPR, pp. 1078–1085. IEEE (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Philips Research Medisys, France

    Rémi Cuingnet, Raphael Prevost, David Lesage, Benoît Mory & Roberto Ardon

  2. CEREMADE, UMR 7534 CNRS, Paris Dauphine University, France

    Raphael Prevost & Laurent D. Cohen

Authors
  1. Rémi Cuingnet
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Raphael Prevost
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David Lesage
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Laurent D. Cohen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Benoît Mory
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Roberto Ardon
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Project Team Asclepios, Inria Sophia Antipolis, 06902, Sophia-Antipolis, France

    Nicholas Ayache & Hervé Delingette & 

  2. MIT, CSAIL, 02139, Cambridge, MA, USA

    Polina Golland

  3. Information and Communication Headquarters, Nagoya University, 464-8603, Nagoya, Japan

    Kensaku Mori

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Cuingnet, R., Prevost, R., Lesage, D., Cohen, L.D., Mory, B., Ardon, R. (2012). Automatic Detection and Segmentation of Kidneys in 3D CT Images Using Random Forests. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33454-2_9

Download citation

  • .RIS
  • .ENW
  • .BIB
  • DOI: https://doi.org/10.1007/978-3-642-33454-2_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33453-5

  • Online ISBN: 978-3-642-33454-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

167.114.118.210

Not affiliated

Springer Nature

© 2023 Springer Nature