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Multi-organ Localization Combining Global-to-Local Regression and Confidence Maps

  • Romane Gauriau
  • Rémi Cuingnet
  • David Lesage
  • Isabelle Bloch
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8675)

Abstract

We propose a method for fast, accurate and robust localization of several organs in medical images. We generalize global-to-local cascades of regression forests [1] to multiple organs. A first regressor encodes global relationships between organs. Subsequent regressors refine the localization of each organ locally and independently for improved accuracy. We introduce confidence maps, which incorporate information about both the regression vote distribution and the organ shape through probabilistic atlases. They are used within the cascade itself, to better select the test voxels for the second set of regressors, and to provide richer information than the classical bounding boxes thanks to the shape prior. We demonstrate the robustness and accuracy of our approach through a quantitative evaluation on a large database of 130 CT volumes.

Keywords

Random Forest Binary Mask Fast Implementation Local Step Probabilistic Atlas 
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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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Romane Gauriau
    • 1
    • 2
  • Rémi Cuingnet
    • 1
  • David Lesage
    • 1
  • Isabelle Bloch
    • 2
  1. 1.Philips Research MediSysParisFrance
  2. 2.Institut Mines-TelecomTelecom ParisTech, CNRS LTCIParisFrance

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