Deep Learning Algorithm for Reducing CT Slice Thickness: Effect on Reproducibility of Radiomic Features in Lung Cancer.

Journal: Korean journal of radiology
Published Date: Oct 1, 2019

Abstract

OBJECTIVE: To retrospectively assess the effect of CT slice thickness on the reproducibility of radiomic features (RFs) of lung cancer, and to investigate whether convolutional neural network (CNN)-based super-resolution (SR) algorithms can improve the reproducibility of RFs obtained from images with different slice thicknesses.

Authors

  • Sohee Park
    Department of Radiology and Research Institute of Radiology, Asan Medical Center, College of Medicine, University of Ulsan, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul, 138736, South Korea.
  • Sang Min Lee
    Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Kyung Hyun Do
    Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • June Goo Lee
    Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Woong Bae
    Soombit.ai, Seoul, South Korea.
  • Hyunho Park
    VUNO, Seoul, Korea.
  • Kyu Hwan Jung
    VUNO Inc., Seoul, Korea.
  • Joon Beom Seo
    Department of Radiology, Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Korea.

Keywords

External Resources

Popular Topics
Recent Journals