Confidence of model based shape reconstruction from sparse data

Authors:
N. Baka;M. de Bruijne;J. H. C. Reiber;W. Niessen;B. P. F. Lelieveldt
Affiliations:
Erasmus Medical Center, Rotterdam, Netherlands and Leiden University Medical Center, Leiden, Netherlands;Erasmus Medical Center, Rotterdam, Netherlands and Department of Computer Science, University of Copenhagen, Denmark;Leiden University Medical Center, Leiden, Netherlands;Erasmus Medical Center, Rotterdam, Netherlands and Delft University of Technology, Delft, Netherlands;Leiden University Medical Center, Leiden, Netherlands and Delft University of Technology, Delft, Netherlands
Venue:
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Year:
2010

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Abstract

Statistical shape models (SSM) are commonly applied for plausible interpolation of missing data in medical imaging. However, when fitting a shape model to sparse information, many solutions may fit the available data. In this paper we derive a constrained SSM to fit noisy sparse input landmarks and assign a confidence value to the resulting reconstructed shape. An evaluation study is performed to compare three methods used for sparse SSM fitting W.r.t. specificity, generalization ability, and correctness of estimated confidence limits with an increasing amount of input information. We find that the proposed constrained shape model outperforms the other models, is robust against the selection and amount of sparse information, and indicates the shape confidence well.