Learning Pit Pattern Concepts for Gastroenterological Training

  • Roland Kwitt
  • Nikhil Rasiwasia
  • Nuno Vasconcelos
  • Andreas Uhl
  • Michael Häfner
  • Friedrich Wrba
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6893)


In this article, we propose an approach to learn the characteristics of colonic mucosal surface structures, the so called pit patterns, commonly observed during high-magnification colonoscopy. Since the discrimination of the pit pattern types usually requires an experienced physician, an interesting question is whether we can automatically find a collection of images which most typically show a particular pit pattern characteristic. This is of considerable practical interest, since it is imperative for gastroenterological training to have a representative image set for the textbook descriptions of the pit patterns. Our approach exploits recent research on semantic image retrieval and annotation. This facilitates to learn a semantic space for the pit pattern concepts which eventually leads to a very natural formulation of our task.


Narrow Band Imaging Semantic Level Semantic Space Average Error Rate Confocal Laser Endomicroscopy 
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 2011

Authors and Affiliations

  • Roland Kwitt
    • 1
  • Nikhil Rasiwasia
    • 2
  • Nuno Vasconcelos
    • 2
  • Andreas Uhl
    • 1
  • Michael Häfner
    • 3
  • Friedrich Wrba
    • 4
  1. 1.Dept. of Computer SciencesUniv. of SalzburgAustria
  2. 2.Dept. of Electrical and Computer EngineeringUniv. of CaliforniaSan DiegoUSA
  3. 3.Dept. of Internal MedicineSt. Elisabeth HospitalViennaAustria
  4. 4.Dept. of Clinical PathologyVienna Medical Univ.Austria

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