Abstract
Statistical shape, appearance, and motion models are widely used as priors in medical image analysis to, for example, constrain image segmentation [1] and motion estimation results [2]. These models try to learn a compact parameterization of the space of plausible object instances from a population of observed samples using low-rank matrix approximation methods (SVD or PCA). The quality of these models heavily depends on the quantity and quality of the training population. As it is usually quite challenging to collect large and representative training populations, models used in practice often suffer from a limited expressiveness
Original language | English |
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Title of host publication | Bildverarbeitung für die Medizin 2017 |
Editors | K.H. Maier-Hein, T.M. Deserno, H. Handels, T. Tolxdorff |
Number of pages | 2 |
Publisher | Springer Vieweg, Berlin Heidelberg |
Publication date | 01.03.2017 |
Pages | 215-216 |
ISBN (Print) | 978-3-662-54344-3 |
ISBN (Electronic) | 978-3-662-54345-0 |
DOIs | |
Publication status | Published - 01.03.2017 |
Event | Bildverarbeitung für die Medizin 2017 - Heidelberg, Germany Duration: 12.03.2017 → 14.03.2017 |