The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Deep-STORM: super-resolution single-molecule microscopy by deep learning
Open accessAbstract
We present an ultra-fast, precise, parameter-free method, which we term Deep-STORM, for obtaining super-resolution images from stochastically-blinking emitters, such as fluorescent molecules used for localization microscopy. Deep-STORM uses a deep convolutional neural network that can be trained on simulated data or experimental measurements, both of which are demonstrated. The method achieves state-of-the-art resolution under challenging signal-to-noise conditions and high emitter densities, and is significantly faster than existing approaches. Additionally, no prior information on the shape of the underlying structure is required, making the method applicable to any blinking data-set. We validate our approach by super-resolution image reconstruction of simulated and experimentally obtained data.
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Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Fiji: an open-source platform for biological-image analysis
Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., et al. (2012) · Nature Methods · doi:10.1038/nmeth.2019 · Cited by 66,890
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Image Super-Resolution Using Deep Convolutional Networks
Dong, C., Loy, C. C., He, K., & Tang, X. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2439281 · Cited by 10,105
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
Kim, J., Lee, J. K., & Lee, K. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.182 · Cited by 7,870
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Wang, G., Ye, J. C., & De Man, B. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00273-z · Cited by 531
Deep learning in ultrasound imaging
van Sloun, R. J. G., Cohen, R., & Eldar, Y. C. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2932116 · Cited by 316
Microfluidic high-throughput 3D cell culture
Ko, J., Park, D., Lee, J., Jung, S., Baek, K., Sung, K. E., Lee, J., & Jeon, N. L. (2024) · Nature Reviews Bioengineering · doi:10.1038/s44222-024-00163-8 · Cited by 88
An alternative approach to nucleic acid memory
Dickinson, G. D., Mortuza, G. M., Clay, W., Piantanida, L., Green, C. M., Watson, C., Hayden, E. J., Andersen, T., Kuang, W., Graugnard, E., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22277-y · Cited by 82
High-Speed Chemical Imaging by Dense-Net Learning of Femtosecond Stimulated Raman Scattering
Zhang, J., Zhao, J., Lin, H., Tan, Y., & Cheng, J.-X. (2020) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.0c01598 · Cited by 54
Metadata
- Authors
- Nehme, E., Weiss, L. E., Michaeli, T., & Shechtman, Y.
- Venue
- Optica
- Publisher
- Optica Publishing Group
- Published
- 2018-04-12
- Volume
- 5
- Issue
- 4
- Pages
- 458
- ISSN
- 2334-2536 (ISSN-L)
- OpenAlex
- W2787020583
- Cited by
- 604
- References
- 7
- FWCI
- 34.38
- Type
- journal article
- Language
- en
- Open access
- gold
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- Nehme, E., Weiss, L. E., Michaeli, T., & Shechtman, Y. (2018). Deep-STORM: super-resolution single-molecule microscopy by deep learning. Optica. https://doi.org/10.1364/optica.5.000458
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