Sanders classification of calcaneal fractures in CT images with deep learning and differential data augmentation techniques.

Journal: Injury
PMID: 32962829

Abstract

BACKGROUND: Classification of the type of calcaneal fracture on CT images is essential in driving treatment. However, human-based classification can be challenging due to anatomical complexities and CT image constraints. The use of computer-aided classification system in standard practice is additionally hindered by the availability of training images. The aims of this study is to 1) propose a deep learning network combined with data augmentation technique to classify calcaneal fractures on CT images into the Sanders system, and 2) assess the efficiency of such approach with differential training methods.

Authors

  • Nurya Aghnia Farda
    Department of Computer Science and Information Engineering, National Central University, Jhongli County, Taoyuan City, Taiwan.
  • Jiing-Yih Lai
    Department of Mechanical Engineering, National Central University, Jhongli County, Taoyuan City, Taiwan.
  • Jia-Ching Wang
  • Pei-Yuan Lee
    Orthopedic Department, Show Chwan Memorial Hospital, Changhua City, Taiwan.
  • Jia-Wei Liu
    Institute of Cognitive Neuroscience, National Central University, Jhongli County, Taoyuan City, Taiwan.
  • I-Hui Hsieh
    Institute of Cognitive Neuroscience, National Central University, Jhongli County, Taoyuan City, Taiwan. Electronic address: ihsieh@cc.ncu.edu.tw.

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