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Semi-Supervised and Active Learning for Automatic Segmentation of Crohn’s Disease

  • Dwarikanath Mahapatra
  • Peter J. Schüffler
  • Jeroen A. W. Tielbeek
  • Franciscus M. Vos
  • Joachim M. Buhmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8150)

Abstract

Our proposed method combines semi supervised learning (SSL) and active learning (AL) for automatic detection and segmentation of Crohn’s disease (CD) from abdominal magnetic resonance (MR) images. Random forest (RF) classifiers are used due to fast SSL classification and capacity to interpret learned knowledge. Query samples for AL are selected by a novel information density weighted approach using context information, semantic knowledge and labeling uncertainty. Experimental results show that our proposed method combines the advantages of SSL and AL, and with fewer samples achieves higher classification and segmentation accuracy over fully supervised methods.

Keywords

Random Forest Semantic Information Label Sample Semantic Knowledge Unlabeled Sample 
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-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Dwarikanath Mahapatra
    • 1
  • Peter J. Schüffler
    • 1
  • Jeroen A. W. Tielbeek
    • 2
  • Franciscus M. Vos
    • 2
    • 3
  • Joachim M. Buhmann
    • 1
  1. 1.Department of Computer ScienceETH ZurichSwitzerland
  2. 2.Department of RadiologyAcademic Medical CenterThe Netherlands
  3. 3.Quantitative Imaging GroupDelft University of TechnologyThe Netherlands

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