Probabilistic Modeling of Landmark Distances and Structure for Anomaly-proof Landmark Detection

Abstract : A combinatorial optimization algorithm for detecting multiple anatomical landmarks is presented. It can determine the positions of over 100 landmarks concurrently, taking spatial correlations of all landmark pairs into account. Provided that a set of landmark candidate lists is given by sensitivity-optimized single-landmark detectors, the proposed algorithm can find the most probable combination of them through solving a MAP estimation-based combinatorial optimization problem. Additionally, it is designed to handle subjects with "segmentation anomaly of the spinal column," a common anatomical anomaly of the spine. The proposed system was evaluated with 156 landmarks in 50 datasets, using virtually created detector output sets. In the result, the algorithm achieved 97.6\% of spinal anomaly estimation accuracy even with 50 points of candidates given per landmark, as well as 96.2\% of accuracy in landmark candidate selection. From these results, usefulness of the proposed algorithm for subjects with spinal anomaly was suggested.
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Pennec, Xavier and Joshi, Sarang and Nielsen, Mads. Proceedings of the Third International Workshop on Mathematical Foundations of Computational Anatomy - Geometrical and Statistical Methods for Modelling Biological Shape Variability, Sep 2011, Toronto, Canada. pp.159-169, 2011
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Contributeur : Xavier Pennec <>
Soumis le : jeudi 15 septembre 2011 - 17:18:58
Dernière modification le : vendredi 16 septembre 2011 - 09:31:27

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Shouhei Hanaoka, Yoshitaka Masutani, Mitsutaka Nemoto, Yukihiro Nomura, Takeharu Yoshikawa, et al.. Probabilistic Modeling of Landmark Distances and Structure for Anomaly-proof Landmark Detection. Pennec, Xavier and Joshi, Sarang and Nielsen, Mads. Proceedings of the Third International Workshop on Mathematical Foundations of Computational Anatomy - Geometrical and Statistical Methods for Modelling Biological Shape Variability, Sep 2011, Toronto, Canada. pp.159-169, 2011. 〈inria-00624109〉

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