Skip to main content

Rapid Multi-organ Segmentation Using Context Integration and Discriminative Models

  • Conference paper
Information Processing in Medical Imaging (IPMI 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7917))

Included in the following conference series:

Abstract

We propose a novel framework for rapid and accurate segmentation of a cohort of organs. First, it integrates local and global image context through a product rule to simultaneously detect multiple landmarks on the target organs. The global posterior integrates evidence over all volume patches, while the local image context is modeled with a local discriminative classifier. Through non-parametric modeling of the global posterior, it exploits sparsity in the global context for efficient detection. The complete surface of the target organs is then inferred by robust alignment of a shape model to the resulting landmarks and finally deformed using discriminative boundary detectors. Using our approach, we demonstrate efficient detection and accurate segmentation of liver, kidneys, heart, and lungs in challenging low-resolution MR data in less than one second, and of prostate, bladder, rectum, and femoral heads in CT scans, in roughly one to three seconds and in both cases with accuracy fairly close to inter-user variability.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Yang, J., Duncan, J.S.: 3D image segmentation of deformable objects with joint shape-intensity prior models using level sets. Medical Image Analysis 8(3), 285–294 (2004)

    Article  Google Scholar 

  2. Zheng, Y., Georgescu, B., Ling, H., Zhou, S.K., Scheuering, M., Comaniciu, D.: Constrained marginal space learning for efficient 3D anatomical structure detection in medical images. In: CVPR, pp. 194–201. IEEE (2009)

    Google Scholar 

  3. Ling, H., Zhou, S.K., Zheng, Y., Georgescu, B., Suehling, M., Comaniciu, D.: Hierarchical, learning-based automatic liver segmentation. In: CVPR (2008)

    Google Scholar 

  4. Zhou, S.K.: Shape regression machine and efficient segmentation of left ventricle endocardium from 2D b-mode echocardiogram. Medical Image Analysis 14(4), 563–581 (2010)

    Article  Google Scholar 

  5. Kohlberger, T., Sofka, M., Zhang, J., Birkbeck, N., Wetzl, J., Kaftan, J., Declerck, J., Zhou, S.K.: Automatic multi-organ segmentation using learning-based segmentation and level set optimization. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 338–345. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  6. Shimizu, A., Ohno, R., Ikegami, T., Kobatake, H., Nawano, S., Smutek, D.: Segmentation of multiple organs in non-contrast 3D abdominal CT images. International Journal of Computer Assisted Radiology and Surgery 2, 135–142 (2007)

    Article  Google Scholar 

  7. Sofka, M., Zhang, J., Zhou, S.K., Comaniciu, D.: Multiple object detection by sequential Monte Carlo and hierarchical detection network. In: CVPR, June 13-18 (2010)

    Google Scholar 

  8. Liu, D., Zhou, S.K., Bernhardt, D., Comaniciu, D.: Search strategies for multiple landmark detection by submodular maximization. In: CVPR. IEEE (2010)

    Google Scholar 

  9. Criminisi, A., Shotton, J., Bucciarelli, S.: Decision forests with long-range spatial context for organ localization in ct volumes. In: MICCAI-PMMIA Workshop (2009)

    Google Scholar 

  10. 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 Workshop MVC. LNCS, vol. 6533, pp. 106–117. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  11. Cuingnet, R., Prevost, R., Lesage, D., Cohen, L.D., Mory, B., Ardon, R.: Automatic detection and segmentation of kidneys in 3D CT images using random forests. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 66–74. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  12. Tu, Z.: Probabilistic boosting-tree: Learning discriminative models for classification, recognition, and clustering. In: ICCV, pp. 1589–1596 (2005)

    Google Scholar 

  13. Friedman, J., Hastie, T., Tibshirani, R.: The elements of statistical learning. Springer Series in Statistics, vol. 1 (2001)

    Google Scholar 

  14. Datar, M., Indyk, P.: Locality-sensitive hashing scheme based on p-stable distributions. In: SCG 2004: Proceedings of the Twentieth Annual Symposium on Computational Geometry, pp. 253–262. ACM Press (2004)

    Google Scholar 

  15. Dasgupta, S., Freund, Y.: Random projection trees and low dimensional manifolds. In: Proceedings of the 40th Annual ACM Symposium on Theory of Computing, STOC 2008, pp. 537–546. ACM, New York (2008)

    Google Scholar 

  16. Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models their training and application. Comput. Vis. Image Underst. 61, 38–59 (1995)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lay, N., Birkbeck, N., Zhang, J., Zhou, S.K. (2013). Rapid Multi-organ Segmentation Using Context Integration and Discriminative Models. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds) Information Processing in Medical Imaging. IPMI 2013. Lecture Notes in Computer Science, vol 7917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38868-2_38

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-38868-2_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38867-5

  • Online ISBN: 978-3-642-38868-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics