Vertebrae Segmentation in 3D CT Images based on a Variational Framework

Kerstin Hammernik; Thomas Ebner; Darko Stern; Martin Urschler; Thomas Pock

doi:10.1007/978-3-319-14148-0_20

Vertebrae Segmentation in 3D CT Images based on a Variational Framework

Kerstin Hammernik, Thomas Ebner, Darko Stern, Martin Urschler, Thomas Pock

Institute of Computer Graphics and Vision (7100)

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

Abstract

Automatic segmentation of 3D vertebrae is a challenging task in medical imaging. In this paper, we introduce a total variation (TV) based framework that incorporates an a priori model, i.e., a vertebral mean shape, image intensity and edge information. The algorithm was evaluated using leave-one-out cross validation on a data set containing ten computed tomography scans and ground truth segmentations provided for the CSI MICCAI 2014 spine and vertebrae segmentation challenge. We achieve promising results in terms of the Dice Similarity Coefficient (DSC) of 0.93±0.04 averaged over the whole data set.

Original language	English
Title of host publication	Recent Advances in Computational Methods and Clinical Applications for Spine Imaging and Clinical Applications for Spine Imaging
Subtitle of host publication	Part VI
Editors	Jianhua Yao, Ben Glocker, Tobias Klinder, Shuo Li
Place of Publication	Switzerland
Publisher	Springer International Publishing AG
Pages	227-233
ISBN (Electronic)	978-3-319-14148-0
ISBN (Print)	978-3-319-14147-3
DOIs	https://doi.org/10.1007/978-3-319-14148-0_20
Publication status	Published - 2015

Publication series

Name	Lecture Notes in Computational Vision and Biomechanics
Publisher	Springer International Publishing
Volume	20

Fields of Expertise

Information, Communication & Computing
Human- & Biotechnology

Treatment code (Nähere Zuordnung)

Basic - Fundamental (Grundlagenforschung)

Cooperations

BioTechMed-Graz

Access to Document

10.1007/978-3-319-14148-0_20

FWF - BIVISION - Bilevel Optimization for Computer Vision
Pock, T.
1/10/13 → 30/09/18
Project: Research project
Klinisch-Forensische Bildgebung - Developing methods to standardize computer-assisted medical MRI datas [Original in Deutsch: Klinisch-Forensische Bildgebung] - Standard_MRT
Urschler, M., Bornik, A. & Bischof, H.
1/12/08 → 31/05/15
Project: Research project

Honourable Mention Award
Urschler, Martin (Recipient), Ebner, Thomas (Recipient), Stern, Darko (Recipient), Pock, Thomas (Recipient) & Hammernik, Kerstin (Recipient), 14 Sep 2014
Prize: Prizes / Medals / Awards

Cite this

Hammernik, K., Ebner, T., Stern, D., Urschler, M., & Pock, T. (2015). Vertebrae Segmentation in 3D CT Images based on a Variational Framework. In J. Yao, B. Glocker, T. Klinder, & S. Li (Eds.), Recent Advances in Computational Methods and Clinical Applications for Spine Imaging and Clinical Applications for Spine Imaging: Part VI (pp. 227-233). (Lecture Notes in Computational Vision and Biomechanics; Vol. 20). Springer International Publishing AG . https://doi.org/10.1007/978-3-319-14148-0_20

Vertebrae Segmentation in 3D CT Images based on a Variational Framework. / Hammernik, Kerstin; Ebner, Thomas; Stern, Darko et al.

Recent Advances in Computational Methods and Clinical Applications for Spine Imaging and Clinical Applications for Spine Imaging: Part VI. ed. / Jianhua Yao; Ben Glocker; Tobias Klinder; Shuo Li. Switzerland : Springer International Publishing AG , 2015. p. 227-233 (Lecture Notes in Computational Vision and Biomechanics; Vol. 20).

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

Hammernik, K, Ebner, T, Stern, D, Urschler, M & Pock, T 2015, Vertebrae Segmentation in 3D CT Images based on a Variational Framework. in J Yao, B Glocker, T Klinder & S Li (eds), Recent Advances in Computational Methods and Clinical Applications for Spine Imaging and Clinical Applications for Spine Imaging: Part VI. Lecture Notes in Computational Vision and Biomechanics, vol. 20, Springer International Publishing AG , Switzerland, pp. 227-233. https://doi.org/10.1007/978-3-319-14148-0_20

Hammernik K, Ebner T, Stern D, Urschler M, Pock T. Vertebrae Segmentation in 3D CT Images based on a Variational Framework. In Yao J, Glocker B, Klinder T, Li S, editors, Recent Advances in Computational Methods and Clinical Applications for Spine Imaging and Clinical Applications for Spine Imaging: Part VI. Switzerland: Springer International Publishing AG . 2015. p. 227-233. (Lecture Notes in Computational Vision and Biomechanics). doi: 10.1007/978-3-319-14148-0_20

Hammernik, Kerstin ; Ebner, Thomas ; Stern, Darko et al. / Vertebrae Segmentation in 3D CT Images based on a Variational Framework. Recent Advances in Computational Methods and Clinical Applications for Spine Imaging and Clinical Applications for Spine Imaging: Part VI. editor / Jianhua Yao ; Ben Glocker ; Tobias Klinder ; Shuo Li. Switzerland : Springer International Publishing AG , 2015. pp. 227-233 (Lecture Notes in Computational Vision and Biomechanics).

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abstract = "Automatic segmentation of 3D vertebrae is a challenging task in medical imaging. In this paper, we introduce a total variation (TV) based framework that incorporates an a priori model, i.e., a vertebral mean shape, image intensity and edge information. The algorithm was evaluated using leave-one-out cross validation on a data set containing ten computed tomography scans and ground truth segmentations provided for the CSI MICCAI 2014 spine and vertebrae segmentation challenge. We achieve promising results in terms of the Dice Similarity Coefficient (DSC) of 0.93±0.04 averaged over the whole data set.",

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Vertebrae Segmentation in 3D CT Images based on a Variational Framework

Abstract

Publication series

Fields of Expertise

Treatment code (Nähere Zuordnung)

Cooperations

Access to Document

Fingerprint

Projects

FWF - BIVISION - Bilevel Optimization for Computer Vision

Klinisch-Forensische Bildgebung - Developing methods to standardize computer-assisted medical MRI datas [Original in Deutsch: Klinisch-Forensische Bildgebung] - Standard_MRT

Prizes

Honourable Mention Award

Cite this