Synthesis of CT images from digital body phantoms using CycleGAN.

Journal: International journal of computer assisted radiology and surgery
Published Date: Aug 5, 2019

Abstract

PURPOSE: The potential of medical image analysis with neural networks is limited by the restricted availability of extensive data sets. The incorporation of synthetic training data is one approach to bypass this shortcoming, as synthetic data offer accurate annotations and unlimited data size.

Authors

  • Tom Russ
    Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor Kutzer Ufer 1-3, 68167 Mannheim, Germany.
  • Stephan Goerttler
    Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
  • Alena-Kathrin Schnurr
    Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor Kutzer Ufer 1-3, 68167 Mannheim, Germany. Electronic address: alena-kathrin.schnurr@medma.uni-heidelberg.de.
  • Dominik F Bauer
    Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
  • Sepideh Hatamikia
    Austrian Center for Medical Innovation and Technology, Vienna, Austria.
  • Lothar R Schad
  • Frank G Zöllner
  • Khanlian Chung
    Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor Kutzer Ufer 1-3, 68167 Mannheim, Germany.

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