A sparse approach to build shape models with routine clinical data
Gutierrez B, Mateus D, Shiban E, Meyer B, Lehmberg J, Navab N (2014)
Publication Type: Conference contribution
Publication year: 2014
Publisher: Institute of Electrical and Electronics Engineers Inc.
Pages Range: 258-261
Conference Proceedings Title: 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
Event location: Beijing, CHN
ISBN: 9781467319591
DOI: 10.1109/isbi.2014.6867858
Abstract
Statistical shape models (SSMs) are widely used for introducing shape priors in medical image analysis. However, building a SSM usually requires careful data acquisitions to gather training datasets with both sufficient quality and enough shape variations. We present a robust framework to build reliable SSMs from a dataset with outliers and incomplete data. Our method is based on Point Distribution Models (PDMs) and makes use of recent advances in sparse optimisation methods to deal with erroneous correspondences. For validation, we apply the proposed approach to a dataset of 43 (including 24 corrupt) CT scans taken during routine clinical practice. We show that our method is able to improve the quality of the skull SSM in terms of generalization ability, specificity, compactness and robustness to missing data in comparison to standard and state-of-the-art algorithms.
Involved external institutions
Germany (DE)How to cite
APA:
Gutierrez, B., Mateus, D., Shiban, E., Meyer, B., Lehmberg, J., & Navab, N. (2014). A sparse approach to build shape models with routine clinical data. In 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 (pp. 258-261). Beijing, CHN: Institute of Electrical and Electronics Engineers Inc..
MLA:
Gutierrez, Benjamin, et al. "A sparse approach to build shape models with routine clinical data." Proceedings of the 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014, Beijing, CHN Institute of Electrical and Electronics Engineers Inc., 2014. 258-261.
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