Abstract: Learning of Representative Multi-Resolution Multi-Object Statistical Shape Models from Small Training Populations

Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Abstract

Statistical shape models learned from a population of training shapes are frequently used as a shape prior. A key problem associated with their training is to provide a representative and large training set of (manual) segmentations. Therefore, models often suffer from the high-dimension-low-sample-size (HDLSS) problem, which limits their expressiveness and directly affects their performance.

Copyright information

© Springer-Verlag GmbH Deutschland 2017

Authors and Affiliations

  1. 1.Institute of Medical InformaticsUniversity of LübeckLübeckDeutschland

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