TY - GEN
T1 - A sparse approach to build shape models with routine clinical data
AU - Gutierrez, Benjamin
AU - Mateus, Diana
AU - Shiban, Ehab
AU - Meyer, Bernhard
AU - Lehmberg, Jens
AU - Navab, Nassir
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/7/29
Y1 - 2014/7/29
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/84927916025
U2 - 10.1109/isbi.2014.6867858
DO - 10.1109/isbi.2014.6867858
M3 - Conference contribution
AN - SCOPUS:84927916025
T3 - 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
SP - 258
EP - 261
BT - 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th IEEE International Symposium on Biomedical Imaging, ISBI 2014
Y2 - 29 April 2014 through 2 May 2014
ER -