Automatic intervertebral disc localization and segmentation in 3D MR images based on regression forests and active contours

Martin Urschler; Kerstin Hammernik; Thomas Ebner; Darko Stern

doi:10.1007/978-3-319-41827-8_13

Automatic intervertebral disc localization and segmentation in 3D MR images based on regression forests and active contours

Martin Urschler^*, Kerstin Hammernik, Thomas Ebner, Darko Stern

^*Corresponding author for this work

Institute of Computer Graphics and Vision (7100)

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

We introduce a fully automatic localization and segmentation pipeline for three-dimensional (3D) intervertebral discs (IVDs), consisting of a regression-based prediction of vertebral bodies and IVD positions as well as a 3D geodesic active contour segmentation delineating the IVDs. The approach was evaluated on the data set of the challenge in conjunction with the 3^rd MICCAI Workshop & Challenge on Computational Methods and Clinical Applications for Spine Imaging -MICCAI– CSI2015, that consists of 15 magnetic resonance images of the lumbar spine with given ground truth segmentations. Based on a localization accuracy of 3.9±1.6 mm, we achieve segmentation results in terms of the Dice similarity coefficient of 89.1 ±2.9% averaged over the whole data set.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher	Springer Verlag Heidelberg
Pages	130-140
Number of pages	11
Volume	9402
ISBN (Print)	9783319418261
DOIs	https://doi.org/10.1007/978-3-319-41827-8_13
Publication status	Published - 2016
Event	International Conference on Medical Image Computing and Computer Assisted Intervention: MICCAI 2015 - Munich, Germany Duration: 5 Oct 2015 → 9 Oct 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9402
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Conference

Conference	International Conference on Medical Image Computing and Computer Assisted Intervention
Country/Territory	Germany
City	Munich
Period	5/10/15 → 9/10/15

ASJC Scopus subject areas

General Computer Science
Theoretical Computer Science

Fields of Expertise

Information, Communication & Computing

Cooperations

BioTechMed-Graz

Access to Document

10.1007/978-3-319-41827-8_13

Cite this

Urschler, M., Hammernik, K., Ebner, T., & Stern, D. (2016). Automatic intervertebral disc localization and segmentation in 3D MR images based on regression forests and active contours. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9402, pp. 130-140). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9402). Springer Verlag Heidelberg. https://doi.org/10.1007/978-3-319-41827-8_13

Automatic intervertebral disc localization and segmentation in 3D MR images based on regression forests and active contours. / Urschler, Martin; Hammernik, Kerstin; Ebner, Thomas et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9402 Springer Verlag Heidelberg, 2016. p. 130-140 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9402).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Urschler, M, Hammernik, K, Ebner, T & Stern, D 2016, Automatic intervertebral disc localization and segmentation in 3D MR images based on regression forests and active contours. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 9402, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9402, Springer Verlag Heidelberg, pp. 130-140, International Conference on Medical Image Computing and Computer Assisted Intervention, Munich, Germany, 5/10/15. https://doi.org/10.1007/978-3-319-41827-8_13

Urschler M, Hammernik K, Ebner T, Stern D. Automatic intervertebral disc localization and segmentation in 3D MR images based on regression forests and active contours. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9402. Springer Verlag Heidelberg. 2016. p. 130-140. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-41827-8_13

Urschler, Martin ; Hammernik, Kerstin ; Ebner, Thomas et al. / Automatic intervertebral disc localization and segmentation in 3D MR images based on regression forests and active contours. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9402 Springer Verlag Heidelberg, 2016. pp. 130-140 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inbook{74dde05e1b4b446b89ad5e94161cbd1e,

title = "Automatic intervertebral disc localization and segmentation in 3D MR images based on regression forests and active contours",

abstract = "We introduce a fully automatic localization and segmentation pipeline for three-dimensional (3D) intervertebral discs (IVDs), consisting of a regression-based prediction of vertebral bodies and IVD positions as well as a 3D geodesic active contour segmentation delineating the IVDs. The approach was evaluated on the data set of the challenge in conjunction with the 3rd MICCAI Workshop & Challenge on Computational Methods and Clinical Applications for Spine Imaging -MICCAI– CSI2015, that consists of 15 magnetic resonance images of the lumbar spine with given ground truth segmentations. Based on a localization accuracy of 3.9±1.6 mm, we achieve segmentation results in terms of the Dice similarity coefficient of 89.1 ±2.9% averaged over the whole data set.",

author = "Martin Urschler and Kerstin Hammernik and Thomas Ebner and Darko Stern",

year = "2016",

doi = "10.1007/978-3-319-41827-8_13",

language = "English",

isbn = "9783319418261",

volume = "9402",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag Heidelberg",

pages = "130--140",

booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

note = "International Conference on Medical Image Computing and Computer Assisted Intervention : MICCAI 2015 ; Conference date: 05-10-2015 Through 09-10-2015",

}

TY - CHAP

T1 - Automatic intervertebral disc localization and segmentation in 3D MR images based on regression forests and active contours

AU - Urschler, Martin

AU - Hammernik, Kerstin

AU - Ebner, Thomas

AU - Stern, Darko

PY - 2016

Y1 - 2016

N2 - We introduce a fully automatic localization and segmentation pipeline for three-dimensional (3D) intervertebral discs (IVDs), consisting of a regression-based prediction of vertebral bodies and IVD positions as well as a 3D geodesic active contour segmentation delineating the IVDs. The approach was evaluated on the data set of the challenge in conjunction with the 3rd MICCAI Workshop & Challenge on Computational Methods and Clinical Applications for Spine Imaging -MICCAI– CSI2015, that consists of 15 magnetic resonance images of the lumbar spine with given ground truth segmentations. Based on a localization accuracy of 3.9±1.6 mm, we achieve segmentation results in terms of the Dice similarity coefficient of 89.1 ±2.9% averaged over the whole data set.

AB - We introduce a fully automatic localization and segmentation pipeline for three-dimensional (3D) intervertebral discs (IVDs), consisting of a regression-based prediction of vertebral bodies and IVD positions as well as a 3D geodesic active contour segmentation delineating the IVDs. The approach was evaluated on the data set of the challenge in conjunction with the 3rd MICCAI Workshop & Challenge on Computational Methods and Clinical Applications for Spine Imaging -MICCAI– CSI2015, that consists of 15 magnetic resonance images of the lumbar spine with given ground truth segmentations. Based on a localization accuracy of 3.9±1.6 mm, we achieve segmentation results in terms of the Dice similarity coefficient of 89.1 ±2.9% averaged over the whole data set.

UR - http://www.scopus.com/inward/record.url?scp=84979211566&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-41827-8_13

DO - 10.1007/978-3-319-41827-8_13

M3 - Chapter

AN - SCOPUS:84979211566

SN - 9783319418261

VL - 9402

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 130

EP - 140

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer Verlag Heidelberg

T2 - International Conference on Medical Image Computing and Computer Assisted Intervention

Y2 - 5 October 2015 through 9 October 2015

ER -

Automatic intervertebral disc localization and segmentation in 3D MR images based on regression forests and active contours

Abstract

Publication series

Conference

ASJC Scopus subject areas

Fields of Expertise

Cooperations

Access to Document

Other files and links

Fingerprint

FWF - FAME - Fully Automatic MRI-based Age Estimation of Adolescents

Cite this

Automatic intervertebral disc localization and segmentation in 3D MR images based on regression forests and active contours

Abstract

Publication series

Conference

ASJC Scopus subject areas

Fields of Expertise

Cooperations

Access to Document

Other files and links

Fingerprint

Projects

FWF - FAME - Fully Automatic MRI-based Age Estimation of Adolescents

Cite this