Journal article

Comparison of Different Approaches for Measuring Tibial Cartilage Thickness

Publication Details
Author(s): Maier J, Black M, Bonaretti S, Bier B, Eskofier B, Choi JH, Levenston M, Gold G, Fahrig R, Maier A
Publication year: 2017
Volume: 14
Journal issue: 2
ISSN: 1613-4516
Language: English


Osteoarthritis is a degenerative disease affecting bones and cartilage especially in the human knee. In this context, cartilage thickness is an indicator for knee cartilage health. Thickness measurements are performed on medical images acquired in-vivo. Currently, there is no standard method agreed upon that defines a distance measure in articular cartilage. In this work, we present a comparison of different methods commonly used in literature. These methods are based on nearest neighbors, surface normal vectors, local thickness and potential field lines. All approaches were applied to manual segmentations of tibia and lateral and medial tibial cartilage performed by experienced raters. The underlying data were contrast agent-enhanced cone-beam C-arm CT reconstructions of one healthy subject’s knee. The subject was scanned three times, once in supine position and two times in a standing weight-bearing position. A comparison of the resulting thickness maps shows similar distributions and high correlation coefficients between the approaches above 0.90. The nearest neighbor method results on average in the lowest cartilage thickness values, while the local thickness approach assigns the highest values. We showed that the different methods agree in their thickness distribution. The results will be used for a future evaluation of cartilage change under weight-bearing conditions.

How to cite
APA: Maier, J., Black, M., Bonaretti, S., Bier, B., Eskofier, B., Choi, J.-H.,... Maier, A. (2017). Comparison of Different Approaches for Measuring Tibial Cartilage Thickness. Journal of integrative bioinformatics, 14(2).

MLA: Maier, Jennifer, et al. "Comparison of Different Approaches for Measuring Tibial Cartilage Thickness." Journal of integrative bioinformatics 14.2 (2017).

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