The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Deep learning enables structured illumination microscopy with low light levels and enhanced speed
Open accessAbstract
Structured illumination microscopy (SIM) surpasses the optical diffraction limit and offers a two-fold enhancement in resolution over diffraction limited microscopy. However, it requires both intense illumination and multiple acquisitions to produce a single high-resolution image. Using deep learning to augment SIM, we obtain a five-fold reduction in the number of raw images required for super-resolution SIM, and generate images under extreme low light conditions (at least 100× fewer photons). We validate the performance of deep neural networks on different cellular structures and achieve multi-color, live-cell super-resolution imaging with greatly reduced photobleaching.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
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
Improved Techniques for Training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016) · arXiv · doi:10.48550/arxiv.1606.03498 · Cited by 6,965
Generative Adversarial Networks: An Overview
Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. (2018) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2765202 · Cited by 4,540
Cited by
Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-021-01614-0 · Cited by 3,212
Deep learning for in vivo near-infrared imaging
Ma, Z., Wang, F., Wang, W., Zhong, Y., & Dai, H. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2021446118 · Cited by 135
Multi-resolution analysis enables fidelity-ensured deconvolution for fluorescence microscopy
Hou, Y., Wang, W., Fu, Y., Ge, X., Li, M., & Xi, P. (2024) · eLight · doi:10.1186/s43593-024-00073-7 · Cited by 42
Metadata
- Authors
- Jin, L., Liu, B., Zhao, F., Hahn, S., Dong, B., Song, R., Elston, T. C., Xu, Y., & Hahn, K. M.
- Venue
- Nature Communications
- Published
- 2020-04-22
- Volume
- 11
- Issue
- 1
- Pages
- 1934-1934
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W3020641185
- Cited by
- 253
- References
- 4
- FWCI
- 14.10
- Type
- journal article
- Language
- en
- Open access
- gold
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- Jin, L., Liu, B., Zhao, F., Hahn, S., Dong, B., Song, R., Elston, T. C., Xu, Y., & Hahn, K. M. (2020). Deep learning enables structured illumination microscopy with low light levels and enhanced speed. Nature Communications. https://doi.org/10.1038/s41467-020-15784-x
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