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

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

Research output: Chapter in Book/Report/Conference proceedingChapter

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.
LanguageEnglish
Title of host publicationRecent Advances in Computational Methods and Clinical Applications for Spine Imaging and Clinical Applications for Spine Imaging
Subtitle of host publicationPart VI
EditorsJianhua Yao, Ben Glocker, Tobias Klinder, Shuo Li
Place of PublicationSwitzerland
PublisherSpringer International Publishing AG
Pages227-233
ISBN (Electronic)978-3-319-14148-0
ISBN (Print)978-3-319-14147-3
DOIs
StatusPublished - 2015

Publication series

NameLecture Notes in Computational Vision and Biomechanics
PublisherSpringer International Publishing
Volume20

Fingerprint

Medical imaging
Tomography

Fields of Expertise

  • Information, Communication & Computing
  • Human- & Biotechnology

Treatment code (Nähere Zuordnung)

  • Basic - Fundamental (Grundlagenforschung)

Cooperations

  • BioTechMed-Graz

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). Switzerland: Springer International Publishing AG . DOI: 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; Urschler, Martin; Pock, Thomas.

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 proceedingChapter

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. DOI: 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). Available from, DOI: 10.1007/978-3-319-14148-0_20
Hammernik, Kerstin ; Ebner, Thomas ; Stern, Darko ; Urschler, Martin ; Pock, Thomas. / 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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