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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer-Verlag GmbH Deutschland
About this paper
Cite this paper
Wilms, M., Handels, H., Ehrhardt, J. (2017). Abstract: Learning of Representative Multi-Resolution Multi-Object Statistical Shape Models from Small Training Populations. In: Maier-Hein, geb. Fritzsche, K., Deserno, geb. Lehmann, T., Handels, H., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2017. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-54345-0_81
Download citation
DOI: https://doi.org/10.1007/978-3-662-54345-0_81
Published:
Publisher Name: Springer Vieweg, Berlin, Heidelberg
Print ISBN: 978-3-662-54344-3
Online ISBN: 978-3-662-54345-0
eBook Packages: Computer Science and Engineering (German Language)