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Reusability report: Feature disentanglement in generating a three-dimensional structure from a two-dimensional slice with sliceGAN

Nature Machine Intelligence volume 3, pages 861–863 (2021)Cite this article

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The Original Article was published on 05 April 2021

arising from Steve Kench & Samuel J. Cooper. Nature Machine Intelligence https://doi.org/10.1038/s42256-021-00322-1 (2021).

Deep generative models are known to be able to model arbitrary probability distributions. Among these, a recent deep generative model, dubbed sliceGAN1, proposed a new way of using the generative adversarial network (GAN) to capture the microstructural characteristics of a two-dimensional (2D) slice and generate three-dimensional (3D) volumes with similar properties. While 3D micrographs are largely beneficial in simulating diverse material behaviour, they are often much harder to obtain than their 2D counterparts. Hence, sliceGAN opens up many interesting directions of research by learning the representative distribution from 2D slices and transferring the learned knowledge to generate arbitrary 3D volumes.

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Fig. 1: Overview of AdaIN-style generator for continuous feature disentanglement of generated 3D volume.
Fig. 2: Generated samples using feature disentangled sliceGAN.

Data availability

We use the data as presented in ref. 7. The data are accessible in ref. 10.

Code availability

The code is available at https://doi.org/10.5281/zenodo.5411387https://github.com/jongcye/SliceGAN_AdaIN11.

References

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  4. Yang, S., Kim, E. Y. & Ye, J. C. Continuous conversion of CT kernel using switchable CycleGAN with AdaIN. IEEE Trans. Med. Imaging (in the press).

  5. Gu, J. & Ye, J. C. AdaIN-based tunable CycleGAN for efficient unsupervised low-dose CT denoising. IEEE Trans. Comput. Imaging 7, 73–85 (2021).

  6. Zhu, J.-Y., Park, T., Isola, P. & Efros, A. A. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proc. IEEE International Conference on Computer Vision 2223–2232 (2017).

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  11. Chung, H. HJ-harry/SliceGAN_AdaIN: v1.1 (v1.1). Zenodo https://doi.org/10.5281/ZENODO.5411387 (2021).

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Acknowledgements

This research was funded by the National Research Foundation of Korea grant NRF-2020R1A2B5B03001980. This work was also supported by the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (project number: 1711137899, KMDF_PR_20200901_0015).

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Authors and Affiliations

  1. Department of Bio and Brain Engineering, KAIST, Daejeon, Korea

    Hyungjin Chung & Jong Chul Ye

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  1. Hyungjin Chung
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  2. Jong Chul Ye
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Contributions

H.C. performed all experiments, wrote the extended code and prepared the manuscript. J.C.Y. supervised the project in conception and discussion and prepared the manuscript.

Corresponding author

Correspondence to Jong Chul Ye.

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The authors declare no competing interests.

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Peer review information Nature Machine Intelligence thanks the anonymous reviewers for their contribution to the peer review of this work.

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Chung, H., Ye, J.C. Reusability report: Feature disentanglement in generating a three-dimensional structure from a two-dimensional slice with sliceGAN. Nat Mach Intell 3, 861–863 (2021). https://doi.org/10.1038/s42256-021-00400-4

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