Statistical shape modeling of pathological scoliotic vertebrae: A comparative analysis

Elias de Oliveira, Marcelo; Reutlinger, Christoph; Zheng, Guoyan; Hasler, Carol-Claudius; Buchler, Philippe (2010). Statistical shape modeling of pathological scoliotic vertebrae: A comparative analysis. In: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference. 10.1109/IEMBS.2010.5627561

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Statistical shape models (SSMs) have been used widely as a basis for segmenting and interpreting complex anatomical structures. The robustness of these models are sensitive to the registration procedures, i.e., establishment of a dense correspondence across a training data set. In this work, two SSMs based on the same training data set of scoliotic vertebrae, and registration procedures were compared. The first model was constructed based on the original binary masks without applying any image pre- and post-processing, and the second was obtained by means of a feature preserving smoothing method applied to the original training data set, followed by a standard rasterization algorithm. The accuracies of the correspondences were assessed quantitatively by means of the maximum of the mean minimum distance (MMMD) and Hausdorf distance (H(D)). Anatomical validity of the models were quantified by means of three different criteria, i.e., compactness, specificity, and model generalization ability. The objective of this study was to compare quasi-identical models based on standard metrics. Preliminary results suggest that the MMMD distance and eigenvalues are not sensitive metrics for evaluating the performance and robustness of SSMs.

Item Type:

Conference or Workshop Item (Paper)


04 Faculty of Medicine > Pre-clinic Human Medicine > Institute for Surgical Technology & Biomechanics ISTB [discontinued]

UniBE Contributor:

Elias de Oliveira, Marcelo, Reutlinger, Christoph, Zheng, Guoyan, Büchler, Philippe




Factscience Import

Date Deposited:

04 Oct 2013 14:08

Last Modified:

05 Dec 2022 14:00

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Web of Science ID:


URI: (FactScience: 199830)

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