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Radiological Physics and Technology

, Volume 11, Issue 3, pp 360–361 | Cite as

Comments on “Novel real-time tumor-contouring method using deep learning to prevent mistracking in X-ray fluoroscopy” by Terunuma et al.

  • Shinichiro Mori
  • Masahiro Endo
Letter to the Editor

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

Ethical approval

This article does not contain any studies performed on human participants. Additionally, this article does not contain any studies performed on animals.

References

  1. 1.
    Terunuma T, Tokui A, Sakae T. Novel real-time tumor-contouring method using deep learning to prevent mistracking in X-ray fluoroscopy. Radiol Phys Technol. 2018;11(1):43–53.CrossRefPubMedGoogle Scholar
  2. 2.
    Patel R, Panfil J, Campana M, et al. Markerless motion tracking of lung tumors using dual-energy fluoroscopy. Med Phys. 2015;42(1):254–62.CrossRefPubMedGoogle Scholar
  3. 3.
    Tanaka R, Sanada S, Sakuta K, et al. Improved accuracy of markerless motion tracking on bone suppression images: preliminary study for image-guided radiation therapy (IGRT). Phys Med Biol. 2015;60(10):N209–18.CrossRefPubMedGoogle Scholar

Copyright information

© Japanese Society of Radiological Technology and Japan Society of Medical Physics 2018

Authors and Affiliations

  1. 1.Research Center for Charged Particle TherapyNational Institute of Radiological SciencesChibaJapan
  2. 2.Association for Nuclear Technology in MedicineTokyoJapan

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