MRI-based surgical planning for lumbar spinal stenosis

Gabriele Abbati; Stefan Bauer; Sebastian Winklhofer; Peter J. Schüffler; Ulrike Held; Jakob M. Burgstaller; Johann Steurer; Joachim M. Buhmann

doi:10.1007/978-3-319-66179-7_14

MRI-based surgical planning for lumbar spinal stenosis

Gabriele Abbati, Stefan Bauer, Sebastian Winklhofer, Peter J. Schüffler, Ulrike Held, Jakob M. Burgstaller, Johann Steurer, Joachim M. Buhmann

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

2 Scopus citations

Abstract

The most common reason for spinal surgery in elderly patients is lumbar spinal stenosis (LSS). For LSS, treatment decisions based on clinical and radiological information as well as personal experience of the surgeon show large variance. Thus a standardized support system is of high value for a more objective and reproducible decision. In this work, we develop an automated algorithm to localize the stenosis causing the symptoms of the patient in magnetic resonance imaging (MRI). With 22 MRI features of each of five spinal levels of 321 patients, we show it is possible to predict the location of lesion triggering the symptoms. To support this hypothesis, we conduct an automated analysis of labeled and unlabeled MRI scans extracted from 788 patients. We confirm quantitatively the importance of radiological information and provide an algorithmic pipeline for working with raw MRI scans. Both code and data are provided for further research at www.spinalstenosis.ethz.ch.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
Editors	Lena Maier-Hein, Alfred Franz, Pierre Jannin, Simon Duchesne, Maxime Descoteaux, D. Louis Collins
Publisher	Springer Verlag
Pages	116-124
Number of pages	9
ISBN (Print)	9783319661780
DOIs	https://doi.org/10.1007/978-3-319-66179-7_14
State	Published - 2017
Externally published	Yes
Event	20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada Duration: 11 Sep 2017 → 13 Sep 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10435 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
Country/Territory	Canada
City	Quebec City
Period	11/09/17 → 13/09/17

Keywords

Deep learning
Lumbar spinal stenosis
Machine learning

Access to Document

10.1007/978-3-319-66179-7_14

Cite this

Abbati, G., Bauer, S., Winklhofer, S., Schüffler, P. J., Held, U., Burgstaller, J. M., Steurer, J., & Buhmann, J. M. (2017). MRI-based surgical planning for lumbar spinal stenosis. In L. Maier-Hein, A. Franz, P. Jannin, S. Duchesne, M. Descoteaux, & D. L. Collins (Eds.), Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings (pp. 116-124). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10435 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-66179-7_14

Abbati, Gabriele ; Bauer, Stefan ; Winklhofer, Sebastian et al. / MRI-based surgical planning for lumbar spinal stenosis. Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. editor / Lena Maier-Hein ; Alfred Franz ; Pierre Jannin ; Simon Duchesne ; Maxime Descoteaux ; D. Louis Collins. Springer Verlag, 2017. pp. 116-124 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "MRI-based surgical planning for lumbar spinal stenosis",

abstract = "The most common reason for spinal surgery in elderly patients is lumbar spinal stenosis (LSS). For LSS, treatment decisions based on clinical and radiological information as well as personal experience of the surgeon show large variance. Thus a standardized support system is of high value for a more objective and reproducible decision. In this work, we develop an automated algorithm to localize the stenosis causing the symptoms of the patient in magnetic resonance imaging (MRI). With 22 MRI features of each of five spinal levels of 321 patients, we show it is possible to predict the location of lesion triggering the symptoms. To support this hypothesis, we conduct an automated analysis of labeled and unlabeled MRI scans extracted from 788 patients. We confirm quantitatively the importance of radiological information and provide an algorithmic pipeline for working with raw MRI scans. Both code and data are provided for further research at www.spinalstenosis.ethz.ch.",

keywords = "Deep learning, Lumbar spinal stenosis, Machine learning",

author = "Gabriele Abbati and Stefan Bauer and Sebastian Winklhofer and Sch{\"u}ffler, {Peter J.} and Ulrike Held and Burgstaller, {Jakob M.} and Johann Steurer and Buhmann, {Joachim M.}",

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year = "2017",

doi = "10.1007/978-3-319-66179-7_14",

language = "English",

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

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pages = "116--124",

editor = "Lena Maier-Hein and Alfred Franz and Pierre Jannin and Simon Duchesne and Maxime Descoteaux and Collins, {D. Louis}",

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}

Abbati, G, Bauer, S, Winklhofer, S, Schüffler, PJ, Held, U, Burgstaller, JM, Steurer, J & Buhmann, JM 2017, MRI-based surgical planning for lumbar spinal stenosis. in L Maier-Hein, A Franz, P Jannin, S Duchesne, M Descoteaux & DL Collins (eds), Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10435 LNCS, Springer Verlag, pp. 116-124, 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017, Quebec City, Canada, 11/09/17. https://doi.org/10.1007/978-3-319-66179-7_14

MRI-based surgical planning for lumbar spinal stenosis. / Abbati, Gabriele; Bauer, Stefan; Winklhofer, Sebastian et al.
Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. ed. / Lena Maier-Hein; Alfred Franz; Pierre Jannin; Simon Duchesne; Maxime Descoteaux; D. Louis Collins. Springer Verlag, 2017. p. 116-124 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10435 LNCS).

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

TY - GEN

T1 - MRI-based surgical planning for lumbar spinal stenosis

AU - Abbati, Gabriele

AU - Bauer, Stefan

AU - Winklhofer, Sebastian

AU - Schüffler, Peter J.

AU - Held, Ulrike

AU - Burgstaller, Jakob M.

AU - Steurer, Johann

AU - Buhmann, Joachim M.

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - The most common reason for spinal surgery in elderly patients is lumbar spinal stenosis (LSS). For LSS, treatment decisions based on clinical and radiological information as well as personal experience of the surgeon show large variance. Thus a standardized support system is of high value for a more objective and reproducible decision. In this work, we develop an automated algorithm to localize the stenosis causing the symptoms of the patient in magnetic resonance imaging (MRI). With 22 MRI features of each of five spinal levels of 321 patients, we show it is possible to predict the location of lesion triggering the symptoms. To support this hypothesis, we conduct an automated analysis of labeled and unlabeled MRI scans extracted from 788 patients. We confirm quantitatively the importance of radiological information and provide an algorithmic pipeline for working with raw MRI scans. Both code and data are provided for further research at www.spinalstenosis.ethz.ch.

AB - The most common reason for spinal surgery in elderly patients is lumbar spinal stenosis (LSS). For LSS, treatment decisions based on clinical and radiological information as well as personal experience of the surgeon show large variance. Thus a standardized support system is of high value for a more objective and reproducible decision. In this work, we develop an automated algorithm to localize the stenosis causing the symptoms of the patient in magnetic resonance imaging (MRI). With 22 MRI features of each of five spinal levels of 321 patients, we show it is possible to predict the location of lesion triggering the symptoms. To support this hypothesis, we conduct an automated analysis of labeled and unlabeled MRI scans extracted from 788 patients. We confirm quantitatively the importance of radiological information and provide an algorithmic pipeline for working with raw MRI scans. Both code and data are provided for further research at www.spinalstenosis.ethz.ch.

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KW - Lumbar spinal stenosis

KW - Machine learning

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DO - 10.1007/978-3-319-66179-7_14

M3 - Conference contribution

AN - SCOPUS:85029525754

SN - 9783319661780

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

SP - 116

EP - 124

BT - Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings

A2 - Maier-Hein, Lena

A2 - Franz, Alfred

A2 - Jannin, Pierre

A2 - Duchesne, Simon

A2 - Descoteaux, Maxime

A2 - Collins, D. Louis

PB - Springer Verlag

T2 - 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017

Y2 - 11 September 2017 through 13 September 2017

ER -

Abbati G, Bauer S, Winklhofer S, Schüffler PJ, Held U, Burgstaller JM et al. MRI-based surgical planning for lumbar spinal stenosis. In Maier-Hein L, Franz A, Jannin P, Duchesne S, Descoteaux M, Collins DL, editors, Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. Springer Verlag. 2017. p. 116-124. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-66179-7_14

MRI-based surgical planning for lumbar spinal stenosis

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