Automatic finger joint detection for volumetric hand imaging

Johannes Bopp; Mathias Unberath; Stefan Steidl; Rebecca Fahrig; Isabelle Oliveira; Arnd Kleyer; Andreas Maier

doi:10.1007/978-3-662-49465-3_20

Automatic finger joint detection for volumetric hand imaging

Johannes Bopp, Mathias Unberath, Stefan Steidl, Rebecca Fahrig, Isabelle Oliveira, Arnd Kleyer, Andreas Maier

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We propose a fully automatic method for robust finger joint detection in T1 weighted magnetic resonance imaging (MRI) sequences for initialization of statistical shape model (SSM) based segmentation. We propose a robust method that only relies on few training samples. Therefore, a parallel-beam forward projection is calculated on the MRI volume. A trained Bagging classifier will detect the joints in 2D which are then splatted into the 3D volume. For evaluation, leave-one-out cross validation was performed. The detection of the joints in 2D yielded a Dice score of 0.67 ± 0.056 with respect to a manually obtained ground truth. For the initialization of SSM-based segmentation algorithms, the results are very promising.

Original language	English (US)
Title of host publication	Bildverarbeitung fur die Medizin 2016
Subtitle of host publication	Algorithmen – Systeme – Anwendungen - Proceedings des Workshops
Editors	Thomas M. Deserno, Heinz Handels, Thomas Tolxdorff, Hans-Peter Meinzer
Publisher	Kluwer Academic Publishers
Pages	104-109
Number of pages	6
ISBN (Print)	9783662494646
DOIs	https://doi.org/10.1007/978-3-662-49465-3_20
State	Published - 2017
Externally published	Yes
Event	Workshops on Image processing for the medicine, 2016 - Berlin, Germany Duration: Mar 13 2016 → Mar 15 2016

Publication series

Name	Informatik aktuell
ISSN (Print)	1431-472X

Conference

Conference	Workshops on Image processing for the medicine, 2016
Country/Territory	Germany
City	Berlin
Period	3/13/16 → 3/15/16

ASJC Scopus subject areas

Modeling and Simulation

Access to Document

10.1007/978-3-662-49465-3_20

Cite this

Bopp, J., Unberath, M., Steidl, S., Fahrig, R., Oliveira, I., Kleyer, A., & Maier, A. (2017). Automatic finger joint detection for volumetric hand imaging. In T. M. Deserno, H. Handels, T. Tolxdorff, & H.-P. Meinzer (Eds.), Bildverarbeitung fur die Medizin 2016: Algorithmen – Systeme – Anwendungen - Proceedings des Workshops (pp. 104-109). (Informatik aktuell). Kluwer Academic Publishers. https://doi.org/10.1007/978-3-662-49465-3_20

Automatic finger joint detection for volumetric hand imaging. / Bopp, Johannes; Unberath, Mathias; Steidl, Stefan et al.
Bildverarbeitung fur die Medizin 2016: Algorithmen – Systeme – Anwendungen - Proceedings des Workshops. ed. / Thomas M. Deserno; Heinz Handels; Thomas Tolxdorff; Hans-Peter Meinzer. Kluwer Academic Publishers, 2017. p. 104-109 (Informatik aktuell).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Bopp, J, Unberath, M, Steidl, S, Fahrig, R, Oliveira, I, Kleyer, A & Maier, A 2017, Automatic finger joint detection for volumetric hand imaging. in TM Deserno, H Handels, T Tolxdorff & H-P Meinzer (eds), Bildverarbeitung fur die Medizin 2016: Algorithmen – Systeme – Anwendungen - Proceedings des Workshops. Informatik aktuell, Kluwer Academic Publishers, pp. 104-109, Workshops on Image processing for the medicine, 2016, Berlin, Germany, 3/13/16. https://doi.org/10.1007/978-3-662-49465-3_20

Bopp J, Unberath M, Steidl S, Fahrig R, Oliveira I, Kleyer A et al. Automatic finger joint detection for volumetric hand imaging. In Deserno TM, Handels H, Tolxdorff T, Meinzer HP, editors, Bildverarbeitung fur die Medizin 2016: Algorithmen – Systeme – Anwendungen - Proceedings des Workshops. Kluwer Academic Publishers. 2017. p. 104-109. (Informatik aktuell). doi: 10.1007/978-3-662-49465-3_20

Bopp, Johannes ; Unberath, Mathias ; Steidl, Stefan et al. / Automatic finger joint detection for volumetric hand imaging. Bildverarbeitung fur die Medizin 2016: Algorithmen – Systeme – Anwendungen - Proceedings des Workshops. editor / Thomas M. Deserno ; Heinz Handels ; Thomas Tolxdorff ; Hans-Peter Meinzer. Kluwer Academic Publishers, 2017. pp. 104-109 (Informatik aktuell).

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abstract = "We propose a fully automatic method for robust finger joint detection in T1 weighted magnetic resonance imaging (MRI) sequences for initialization of statistical shape model (SSM) based segmentation. We propose a robust method that only relies on few training samples. Therefore, a parallel-beam forward projection is calculated on the MRI volume. A trained Bagging classifier will detect the joints in 2D which are then splatted into the 3D volume. For evaluation, leave-one-out cross validation was performed. The detection of the joints in 2D yielded a Dice score of 0.67 ± 0.056 with respect to a manually obtained ground truth. For the initialization of SSM-based segmentation algorithms, the results are very promising.",

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AU - Maier, Andreas

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Automatic finger joint detection for volumetric hand imaging

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