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"Machine learning for image analysis in the cervical spine: Systematic review of the available models and methods"
C.M.W Goedmakers, L.M. Pereboom, J.W. Schoones, M.L. de Leeuw den Bouter, R.F. Remis, M. Staring and C.L.A. Vleggeert-Lankamp
Abstract
Highlights - Neural network approaches show the most potential for automated image analysis of thecervical spine.
- Fully automatic convolutional neural network (CNN) models are promising Deep Learning methods for segmentation.
- In cervical spine analysis, the biomechanical features are most often studied using finiteelement models.
- The application of artificial neural networks and support vector machine models looks promising for classification purposes.
- This article provides an overview of the methods for research on computer aided imaging diagnostics of the cervical spine.
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Copyright © 2022 by the authors.
Published version © 2022 by .
Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.
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BibTeX entry
| @article{Goedmakers:2022, |
| author |
= {Goedmakers, C.M.W and Pereboom, L.M. and Schoones, J.W. and de Leeuw den Bouter, M.L. and Remis, R.F. and Staring, M. and Vleggeert-Lankamp, C.L.A.}, |
| title |
= {Machine learning for image analysis in the cervical spine: Systematic review of the available models and methods}, |
| journal |
= {Brain and Spine}, |
| volume |
= {2}, |
| pages |
= {101666}, |
| year |
= {2022}, |
| } |
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| last modified: 21-11-2022 |
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