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Regression Forests for Efficient Anatomy Detection and Localization in CT Studies

  • Antonio Criminisi
  • Jamie Shotton
  • Duncan Robertson
  • Ender Konukoglu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6533)

Abstract

This paper proposes multi-class random regression forests as an algorithm for the efficient, automatic detection and localization of anatomical structures within three-dimensional CT scans.

Regression forests are similar to the more popular classification forests, but trained to predict continuous outputs. We introduce a new, continuous parametrization of the anatomy localization task which is effectively addressed by regression forests. This is shown to be a more natural approach than classification.

A single pass of our probabilistic algorithm enables the direct mapping from voxels to organ location and size; with training focusing on maximizing the confidence of output predictions. As a by-product, our method produces salient anatomical landmarks; i.e. automatically selected “anchor” regions which help localize organs of interest with high confidence. Quantitative validation is performed on a database of 100 highly variable CT scans. Localization errors are shown to be lower (and more stable) than those from global affine registration approaches. The regressor’s parallelism and the simplicity of its context-rich visual features yield typical runtimes of only 1s. Applications include semantic visual navigation, image tagging for retrieval, and initializing organ-specific processing.

Keywords

Random Forest Regression Tree Compute Tomography Study Output Prediction Tree Depth 
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.
    Breiman, L.: Random forests. Technical Report TR567, UC Berkeley (1999)Google Scholar
  2. 2.
    Zhou, S.K., Zhou, J., Comaniciu, D.: A boosting regression approach to medical anatomy detection. In: IEEE CVPR, pp. 1–8 (2007)Google Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    Hardle, W.: Applied non-parametric regression. Cambridge University Press, Cambridge (1990)CrossRefzbMATHGoogle Scholar
  5. 5.
    Zhou, S., Georgescu, B., Zhou, X., Comaniciu, D.: Image-based regression using boosting method. In: ICCV (2005)Google Scholar
  6. 6.
    Friedman, J.: Greedy function approximation: A gradient boosting machine. The Annals of Statistics 2(28) (2001)Google Scholar
  7. 7.
    Vapnik, V.: The nature of statistical learning theory. Springer, Heidelberg (2000)CrossRefzbMATHGoogle Scholar
  8. 8.
    Yin, P., Criminisi, A., Essa, I., Winn, J.: Tree-based classifiers for bilayer video segmentation. In: CVPR (2007)Google Scholar
  9. 9.
    Montillo, A., Ling, H.: Age regression from faces using random forests. In: ICIP (2009)Google Scholar
  10. 10.
    Gall, J., Lempitsky, V.: Class-specific Hough forest for object detection. In: IEEE CVPR, Miami (2009)Google Scholar
  11. 11.
    Zhan, Y., Zhou, X.S., Peng, Z., Krishnan, A.: Active scheduling of organ detection and segmentation in whole-body medical images. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 313–321. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. 12.
    Torralba, A., Murphy, K.P., Freeman, W.T.: Sharing visual features for multiclass and multiview object detection. IEEE Trans. PAMI (2007)Google Scholar
  13. 13.
    Seifert, S., Barbu, A., Zhou, S.K., Liu, D., Feulner, J., Huber, M., Sühling, M., Cavallaro, A., Comaniciu, D.: Hierarchical parsing and semantic navigation of full body CT data. In: Pluim, J.P.W., Dawant, B.M. (eds.) SPIE (2009)Google Scholar
  14. 14.
    Criminisi, A., Shotton, J., Bucciarelli, S.: Decision forests with long-range spatial context for organ localization in CT volumes. In: MICCAI Workshop on Probabilistic Models for Medical Image Analysis (2009)Google Scholar
  15. 15.
    Shimizu, A., Ohno, R., Ikegami, T., Kobatake, H.: Multi-organ segmentation in three-dimensional abdominal CT images. Int. J. CARS 1 (2006)Google Scholar
  16. 16.
    Yao, C., Wada, T., Shimizu, A., Kobatake, H., Nawano, S.: Simultaneous location detection of multi-organ by atlas-guided eigen-organ method in volumetric medical images. Int. J. CARS 1 (2006)Google Scholar
  17. 17.
    Isgum, I., Staring, M., Rutten, A., Prokop, M., Viergever, M.A., van Ginneken, B.: Multi-atlas-based segmentation with local decision fusion—application to cardiac and aortic segmentation in ct scans. IEEE Trans. Medical Imaging 28(7) (2009)Google Scholar
  18. 18.
    Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and Regression Trees. Chapman and Hall/CRC (1984)Google Scholar
  19. 19.
    Shotton, J., Winn, J., Rother, C., Criminisi, A.: Textonboost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context. In: IJCV (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Antonio Criminisi
    • 1
  • Jamie Shotton
    • 1
  • Duncan Robertson
    • 1
  • Ender Konukoglu
    • 1
  1. 1.Microsoft Research LtdCambridgeUK

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