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Machine learning for image analysis in the cervical spine: Systematic review of the available models and methods.
Goedmakers, C M W; Pereboom, L M; Schoones, J W; de Leeuw den Bouter, M L; Remis, R F; Staring, M; Vleggeert-Lankamp, C L A.
Afiliação
  • Goedmakers CMW; Department of Neurosurgery, Leiden University Medical Center, Leiden, the Netherlands.
  • Pereboom LM; Computational Neuroscience Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • Schoones JW; Faculty of Mechanical, Maritime and Materials Engineering (3mE), Delft University of Technology, Delft, the Netherlands.
  • de Leeuw den Bouter ML; Walaeus Library, Leiden University Medical Center, Leiden, the Netherlands.
  • Remis RF; Delft Institute of Applied Mathematics, Department of Numerical Analysis, Delft University of Technology, Delft, the Netherlands.
  • Staring M; Circuits and Systems Group, Microelectronics Department, Delft University of Technology, Delft, the Netherlands.
  • Vleggeert-Lankamp CLA; Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands.
Brain Spine ; 2: 101666, 2022.
Article em En | MEDLINE | ID: mdl-36506292
ABSTRACT
•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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Systematic_reviews Idioma: En Revista: Brain Spine Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Holanda

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Systematic_reviews Idioma: En Revista: Brain Spine Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Holanda
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