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

Radiological Physics and Technology volume 11, pages362–363(2018)Cite this article

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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.

    Article  PubMed  Google 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.

    Article  PubMed  Google Scholar 

  4. 4.

    Zhong Z, Zheng L, Kang G, et al. Random Erasing Data Augmentation. 2017

  5. 5.

    Vries TD, Taylor GW. Improved regularization of convolutional neural networks with cutout. 2017

  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.

    Article  Google Scholar 

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Affiliations

  1. Faculty of Medicine, University of Tsukuba, Ten-nohdai 1-1-1, Tsukuba, 305-8575, Japan

    Toshiyuki Terunuma & Takeji Sakae

Authors
  1. Toshiyuki Terunuma
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  2. Takeji Sakae
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Correspondence to Toshiyuki Terunuma.

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

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Terunuma, T., Sakae, T. Response to “Comments on ‘Novel real-time tumor-contouring method using deep learning to prevent mistracking in X-ray fluoroscopy”’. Radiol Phys Technol 11, 362–363 (2018). https://doi.org/10.1007/s12194-018-0471-4

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