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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
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)

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

Machine learning Deep learning Lumbar spinal stenosis 

Notes

Acknowledgments

This research was partially supported by the Max Planck ETH Center for Learning Systems, the SystemsX.ch project SignalX, the Baugarten Foundation, the Helmut Horten Foundation, the Pfizer-Foundation for geriatrics & research in geriatrics, the Symphasis Charitable Foundation, the OPO Foundation, NIH/NCI Cancer Center Support Grant P30 CA008748 and an Oxford - Google DeepMind scholarship.

Supplementary material

455908_1_En_14_MOESM1_ESM.pdf (320 kb)
Supplementary material 1 (pdf 320 KB)

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Gabriele Abbati
    • 1
  • Stefan Bauer
    • 2
  • Sebastian Winklhofer
    • 3
  • Peter J. Schüffler
    • 4
  • Ulrike Held
    • 5
  • Jakob M. Burgstaller
    • 5
  • Johann Steurer
    • 5
  • Joachim M. Buhmann
    • 2
  1. 1.Department of Engineering ScienceUniversity of OxfordOxfordUK
  2. 2.Department of Computer ScienceETH ZürichZürichSwitzerland
  3. 3.NeuroradiologyUniversity Hospital ZürichZürichSwitzerland
  4. 4.Computational PathologyMemorial Sloan Kettering Cancer CenterNew YorkUSA
  5. 5.Horten Centre for Patient Oriented Research and Knowledge TransferUniversity of ZürichZürichSwitzerland

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