Quantitative Comparison of Generative Shape Models for Medical Images

  • Hristina UzunovaEmail author
  • Paul Kaftan
  • Matthias Wilms
  • Nils D. Forkert
  • Heinz Handels
  • Jan Ehrhardt
Conference paper
Part of the Informatik aktuell book series (INFORMAT)


Generative shape models play an important role in medical image analysis. Conventional methods like PCA-based statistical shape models (SSMs) and their various extensions have shown great success modeling natural shape variations in medical images, despite their limitations. Corresponding deep learning-based methods like (variational) autoencoders are well known to overcome many of those limitations. In this work, we compare two conventional and two deep learning-based generative shape modeling approaches to shed light on their limitations and advantages. Experiments on a publicly available 2D chest X-ray data set show that the deep learning methods achieve better specificity and generalization abilities for large training set sizes. However, for smaller training sets, the conventional SSMs are more robust and their latent space is more compact and easier to interpret.


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Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2020

Authors and Affiliations

  1. 1.Institute of Medical InformaticsUniversity of LübeckLübeckDeutschland
  2. 2.Department of RadiologyUniversity of CalgaryCalgaryKanada

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