Learning pit pattern concepts for gastroenterological training

Authors:
Roland Kwitt;Nikhil Rasiwasia;Nuno Vasconcelos;Andreas Uhl;Michael Häfner;Friedrich Wrba
Affiliations:
Dept. of Computer Sciences, Univ. of Salzburg, Austria;Dept. of Electrical and Computer Engineering, Univ. of California, San Diego;Dept. of Electrical and Computer Engineering, Univ. of California, San Diego;Dept. of Computer Sciences, Univ. of Salzburg, Austria;Dept. of Internal Medicine, St. Elisabeth Hospital, Vienna, Austria;Dept. of Clinical Pathology, Vienna Medical Univ., Austria
Venue:
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Year:
2011

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Abstract

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.