Prediction of Trabecular Bone Anisotropy from Quantitative Computed Tomography using Supervised Learning and a Novel Morphometric Feature Descriptor

Chandran, Vimal; Zysset, Philippe; Reyes, Mauricio (October 2015). Prediction of Trabecular Bone Anisotropy from Quantitative Computed Tomography using Supervised Learning and a Novel Morphometric Feature Descriptor. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015. 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I, 9349, pp. 621-628. Springer International Publishing 10.1007/978-3-319-24553-9_76

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Patient-specific biomechanical models including local bone mineral density and anisotropy have gained importance for assessing musculoskeletal disorders. However the trabecular bone anisotropy captured by high-resolution imaging is only available at the peripheral skeleton in clinical practice. In this work, we propose a supervised learning approach to predict trabecular bone anisotropy that builds on a novel set of pose invariant feature descriptors. The statistical relationship between trabecular bone anisotropy and feature descriptors were learned from a database of pairs of high resolution QCT and clinical QCT reconstructions. On a set of leave-one-out experiments, we compared the accuracy of the proposed approach to previous ones, and report a mean prediction error of 6% for the tensor norm, 6% for the degree of anisotropy and 19◦ for the principal tensor direction. These findings show the potential of the proposed approach to predict trabecular bone anisotropy from clinically available QCT images.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

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

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Chandran, Vimal; Zysset, Philippe and Reyes, Mauricio

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health

ISSN:

0302-9743

ISBN:

978-3-319-24552-2

Series:

Lecture Notes in Computer Science

Publisher:

Springer International Publishing

Language:

English

Submitter:

Vimal Chandran

Date Deposited:

11 Feb 2016 15:11

Last Modified:

19 Nov 2016 02:30

Publisher DOI:

10.1007/978-3-319-24553-9_76

BORIS DOI:

10.7892/boris.75431

URI:

https://boris.unibe.ch/id/eprint/75431

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