Fast Multiple Organ Detection and Localization in Whole-Body MR Dixon Sequences

  • Olivier Pauly
  • Ben Glocker
  • Antonio Criminisi
  • Diana Mateus
  • Axel Martinez Möller
  • Stephan Nekolla
  • Nassir Navab
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6893)

Abstract

Automatic localization of multiple anatomical structures in medical images provides important semantic information with potential benefits to diverse clinical applications. Aiming at organ-specific attenuation correction in PET/MR imaging, we propose an efficient approach for estimating location and size of multiple anatomical structures in MR scans. Our contribution is three-fold: (1) we apply supervised regression techniques to the problem of anatomy detection and localization in whole-body MR, (2) we adapt random ferns to produce multi-dimensional regression output and compare them with random regression forests, and (3) introduce the use of 3D LBP descriptors in multi-channel MR Dixon sequences. The localization accuracy achieved with both fern- and forest-based approaches is evaluated by direct comparison with state of the art atlas-based registration, on ground-truth data from 33 patients. Our results demonstrate improved anatomy localization accuracy with higher efficiency and robustness.

Keywords

Random Forest Attenuation Correction Statistical Shape Model Regression Forest Voxel Location 
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.
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References

  1. 1.
    Judenhofer, M.S., et al.: Simultaneous pet-mri: a new approach for functional and morphological imaging. Nature Medicine, 459–465 (2008)Google Scholar
  2. 2.
    Kinahan, P., Hasegawa, B., Beyer, T.: X-ray-based attenuation correction for positron emission tomography/computed tomography scanners. Semin. Nuc. Med. (2003)Google Scholar
  3. 3.
    Kops, E.R., Herzog, H.: Template-based attenuation correction of PET in hybrid MR-PET. Journal of Nuclear Medicine (2008)Google Scholar
  4. 4.
    Hofmann, M., Steinke, F., Scheel, V., Charpiat, G., Farquhar, J., Aschoff, P., Brady, M., Schölkopf, B., Pichler, B.J.: MRI-Based Attenuation Correction for PET/MRI: A Novel Approach Combining Pattern Recognition and Atlas Registration. Journal of Nuclear Medicine (2008)Google Scholar
  5. 5.
    Martinez-Möller, A., Souvatzoglou, M., Delso, G., Bundschuh, R.A., Chefd’hotel, C., Ziegler, S.I., Navab, N., Schwaiger, M., Nekolla, S.G.: Tissue Classification as a Potential Approach for Attenuation Correction in Whole-Body PET/MRI: Evaluation with PET/CT Data. Journal of Nuclear Medicine 50, 520–526 (2009)CrossRefGoogle Scholar
  6. 6.
    Viola, P., Jones, M.J.: Robust real-time face detection. International Journal on Computer Vision 57, 137–154 (2004)CrossRefGoogle Scholar
  7. 7.
    Zhou, S.K., Zhou, J., Comaniciu, D.: A boosting regression approach to medical anatomy detection. In: IEEE Conference on Computer Vision and Pattern Recognition (2007)Google Scholar
  8. 8.
    Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D.: Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features. IEEE Transactions on Medical Imaging 27, 1668–1681 (2008)CrossRefGoogle Scholar
  9. 9.
    Zheng, Y., Bogdan, C.D.: Marginal Space Learning for Efficient Detection of 2D/3D Anatomical Structures in Medical Images. In: Prince, J.L., Pham, D.L., Myers, K.J. (eds.) IPMI 2009. LNCS, vol. 5636, pp. 411–422. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  10. 10.
    Criminisi, A., Shotton, J., Robertson, D., Konukoglu, E.: Regression Forests for Efficient Anatomy Detection and Localization in CT Studies. In: Jiang, T., Navab, N., Pluim, J., Viergever, M. (eds.) MICCAI 2010 Workshop in Medical Computer Vision (2010)Google Scholar
  11. 11.
    Özuysal, M., Calonder, M., Lepetit, V., Fua, P.: Fast Keypoint Recognition using Random Ferns. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, 448–461 (2010)CrossRefGoogle Scholar
  12. 12.
    Breiman, L.: Random forests. Machine Learning 45, 5–32 (2001)CrossRefMATHGoogle Scholar
  13. 13.
    Ma, J.: Dixon Techniques for Water and Fat Imaging. J. Mag. Res. Im. (2008)Google Scholar
  14. 14.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognition 29, 51–59 (1996)CrossRefGoogle Scholar
  15. 15.
    Dollar, P., Welinder, P., Perona, P.: Cascaded pose regression. In: IEEE Conference on Computer Vision and Pattern Recognition (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Olivier Pauly
    • 1
  • Ben Glocker
    • 2
  • Antonio Criminisi
    • 2
  • Diana Mateus
    • 1
  • Axel Martinez Möller
    • 3
  • Stephan Nekolla
    • 3
  • Nassir Navab
    • 1
  1. 1.Computer Aided Medical ProceduresTechnische Universität MünchenGermany
  2. 2.Microsoft Research Ltd.CambridgeUK
  3. 3.Nuklearmedizin, Klinikum Rechts der IsarTechnische Universität MünchenGermany

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