Advertisement

Response to “Comments on ‘Novel real-time tumor-contouring method using deep learning to prevent mistracking in X-ray fluoroscopy”’

  • Toshiyuki Terunuma
  • Takeji Sakae
Author’s reply to Letter to the Editor
  • 22 Downloads

To the editor,

We would like to respond to the comments of Drs. Mori and Endo [1] on our research paper [2].

First, we wish to discuss their suggestion on the requirement for using actual fluoroscopic images in the test stage. The necessity to validate our method using clinical fluoroscopic imaging was already mentioned in “Abstract”, “Discussion”, and “Conclusion” [2]. The sentences in “Discussion” include the following: “we understand that our results were obtained from preliminary simulated fluoroscopic images, and we must validate this method using real clinical fluoroscopy. The anticipated primary difficulty is the different image qualities between the DRRs and the clinical fluoroscopy images. However, we expect that this problem can be solved by improving the DRR quality to be similar to the quality of clinical fluoroscopy images, or by creating a wide contrast variation in the training images for the input data set of deep learning” [2]. The last sentence might be too...

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

Ethical statement

This article does not contain any studies performed on human participants and animals.

References

  1. 1.
    Mori S, Endo M Comments on “Novel real-time tumor-contouring method using deep learning to prevent mistracking in X-ray fluoroscopy” by Terunuma et al. Radiol Phys Technol 2018.  https://doi.org/10.1007/s12194-018-0447-4.
  2. 2.
    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.  https://doi.org/10.1007/s12194-017-0435-0.CrossRefPubMedGoogle Scholar
  3. 3.
    Mori S, Karube M, Shirai T, et al. Carbon-ion pencil beam scanning treatment with gated markerless tumor tracking: an analysis of positional accuracy. Int J Radiat Oncol Biol Phys. 2016;95(1):258–66.  https://doi.org/10.1016/j.ijrobp.2016.01.014.CrossRefPubMedGoogle Scholar
  4. 4.
    Zhong Z, Zheng L, Kang G, et al. Random Erasing Data Augmentation. 2017Google Scholar
  5. 5.
    Vries TD, Taylor GW. Improved regularization of convolutional neural networks with cutout. 2017Google Scholar
  6. 6.
    Meyer P, Noblet V, Mazzara C, et al. Survey on deep learning for radiotherapy. Comp Biol Med. 2018;98:126–46.  https://doi.org/10.1016/j.compbiomed.2018.05.018.CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Faculty of MedicineUniversity of TsukubaTsukubaJapan

Personalised recommendations