Semi-supervised learning for automatic segmentation of the knee from MRI with convolutional neural networks.

Journal: Computer methods and programs in biomedicine
Published Date: Jan 11, 2020

Abstract

BACKGROUND AND OBJECTIVE: Segmentation is a crucial step in multiple biomechanics and orthopedics applications. The time-intensiveness and expertise requirements of medical image segmentation present a significant bottleneck for corresponding workflows. The current study develops and evaluates convolutional neural networks (CNNs) for automatic segmentation of magnetic resonance imaging (MRI) with the objective of assessing their utility for use in biomechanics research methods.

Authors

  • William Burton
    Center for Orthopaedic Biomechanics, University of Denver, 2155 E Wesley Avenue, Denver, CO, USA. Electronic address: will.burton@du.edu.
  • Casey Myers
    Center for Orthopaedic Biomechanics, University of Denver, 2155 E Wesley Avenue, Denver, CO, USA. Electronic address: casey.myers@du.edu.
  • Paul Rullkoetter
    Center for Orthopaedic Biomechanics, University of Denver, 2155 E Wesley Avenue, Denver, CO, USA. Electronic address: paul.rullkoetter@du.edu.

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