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Global voxel transformer networks for augmented microscopy

Nature Machine Intelligence volume 3, pages 161–171 (2021)Cite this article

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A preprint version of the article is available at arXiv.

Abstract

Advances in deep learning have led to remarkable success in augmented microscopy, enabling us to obtain high-quality microscope images without using expensive microscopy hardware and sample preparation techniques. Current deep learning models for augmented microscopy are mostly U-Net-based neural networks, thus sharing certain drawbacks that limit the performance. In particular, U-Nets are composed of local operators only and lack dynamic non-local information aggregation. In this work, we introduce global voxel transformer networks (GVTNets), a deep learning tool for augmented microscopy that overcomes intrinsic limitations of the current U-Net-based models and achieves improved performance. GVTNets are built on global voxel transformer operators, which are able to aggregate global information, as opposed to local operators like convolutions. We apply the proposed methods on existing datasets for three different augmented microscopy tasks under various settings.

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Fig. 1: GVTNets architecture, training and inference.
Fig. 2: GVTNets on label-free prediction of 3D fluorescence images from transmitted-light microscopy.
Fig. 3: GVTNets on content-aware 3D image denoising.
Fig. 4: GVTNets on content-aware 3D-to-2D image projection.
Fig. 5: Generalization ability of GVTNets.

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Data availability

Datasets for label-free prediction of 3D fluorescence images from transmitted-light microscopy25 can be downloaded from https://downloads.allencell.org/publication-data/label-free-prediction/index.html. Datasets for context-aware 3D image denoising and 3D-to-2D image projection27 can be downloaded from https://publications.mpi-cbg.de/publications-sites/7207.

Code availability

The code for GVTNets training, prediction and evaluation (in Python/TensorFlow) is publicly available at https://github.com/divelab/GVTNets and ref. 60.

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Acknowledgements

We thank the teams at CARE and the Allen Institute for Cell Science for making their data and tools publicly available. This work was supported in part by National Science Foundation grants DBI-1922969, IIS-1908166 and IIS-1908220, National Institutes of Health grant 1R21NS102828 and Defense Advanced Research Projects Agency grant N66001-17-2-4031.

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  1. These authors contributed equally: Zhengyang Wang, Yaochen Xie.

Authors and Affiliations

  1. Texas A&M University, Department of Computer Science and Engineering, College Station, TX, USA

    Zhengyang Wang, Yaochen Xie & Shuiwang Ji

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  1. Zhengyang Wang
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Contributions

S.J. conceived and initiated the research. Z.W. and S.J. designed the methods. Z.W. and Y.X. implemented the training and validation methods. Z.W. and Y.X. designed and developed the software package. S.J. supervised the project. Z.W., Y.X. and S.J. wrote the manuscript.

Corresponding author

Correspondence to Shuiwang Ji.

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The authors declare no competing interests.

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Peer review information Nature Machine Intelligence thanks Ruogu Fang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Information

Supplementary Figs. 1–13, Tables 1–6 and Notes 1,2.

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Wang, Z., Xie, Y. & Ji, S. Global voxel transformer networks for augmented microscopy. Nat Mach Intell 3, 161–171 (2021). https://doi.org/10.1038/s42256-020-00283-x

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