Machine learning for image analysis in the cervical spine: Systematic review of the available models and methods

Brain and Spine, vol. 2, pp. 101666

Abstract

• Neural network approaches show the most potential for automated image analysis of the cervical spine, with smaller errors, less computation time and improved robustness when compared to the clinical standard. • In segmentation models, Deep Learning methods show promising results with the application of fully automatic convolutional neural network (CNN) models for CT and MR imaging. • In cervical spine analysis, the biomechanical features are most often studied using finite element models. The application of artificial neural networks and support vector machine models looks promising for other classification purposes. • Machine Learning is the future of This systematic review may function as a reference paper, providing an overview of all available methods reported in literature for authors conducting research on computer aided diagnostics of the cervical spine.

Authors 7

1. Caroline M.W. Goedmakers corresponding Aachen Department of Neurosurgery, Leiden University Medical Center

   Brigham and Women's Hospital · Harvard University · Leiden University Medical Center

   Affiliation as printed

   Computational Neuroscience Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA

   Department of Neurosurgery, Leiden University Medical Center, Leiden, the Netherlands

2. Leonie Pereboom

   Delft University of Technology

   Affiliation as printed

   Faculty of Mechanical, Maritime and Materials Engineering (3mE), Delft University of Technology, Delft, the Netherlands

3. Jan W. Schoones Aachen Walaeus Library

   Leiden University Medical Center

   Affiliation as printed

   Walaeus Library, Leiden University Medical Center, Leiden, the Netherlands

4. M. L. de Leeuw den Bouter

   Delft University of Technology

   Affiliation as printed

   Delft Institute of Applied Mathematics, Department of Numerical Analysis, Delft University of Technology, Delft, the Netherlands

5. R.F. Remis

   Delft University of Technology

   Affiliation as printed

   Circuits and Systems Group, Microelectronics Department, Delft University of Technology, Delft, the Netherlands

6. Marius Staring Aachen Department of Radiology, Leiden University Medical Center

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands

   Intelligent Systems Department, Delft University of Technology, Delft, the Netherlands

7. Carmen L. A. Vleggeert-Lankamp Aachen Department of Neurosurgery, Leiden University Medical Center

   Leiden University Medical Center · Medisch Centrum Haaglanden · Stichting Haaglanden Medisch Centrum · Haga Hospital · Spaarne Ziekenhuis · Spaarne Gasthuis

   Affiliation as printed

   Department of Neurosurgery Haaglanden Medical Centre and HAGA Teaching Hospitals, The Hague, the Netherlands

   Department of Neurosurgery, Leiden University Medical Center, Leiden, the Netherlands

   Department of Neurosurgery, Spaarne Gasthuis Haarlem/Hoofddorp, the Netherlands