Researching | Video-level and high-fidelity super-resolution SIM reconstruction enabled by deep learning

Advanced Imaging, Volume. 1, Issue 1, 011001(2024)

Video-level and high-fidelity super-resolution SIM reconstruction enabled by deep learning

Hanchu Ye¹, Zitong Ye¹, Yunbo Chen¹, Jinfeng Zhang¹, Xu Liu¹, Cuifang Kuang^1,2,3、*, Youhua Chen^1,3、*, and Wenjie Liu^1,2、*

Author Affiliations

¹State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China

²Zhejiang Lab, Hangzhou, China

³Ningbo Innovation Center, Zhejiang University, Ningbo, China

show less

Full Text Get PDF

Figures&Tables (7)

References (34)

Paper Information

References(34)

[1] M. G. L. Gustafsson. Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy. J. Micros., 198, 82(2000).

[2] A. Markwirth et al. Video-rate multi-color structured illumination microscopy with simultaneous real-time reconstruction. Nat. Commun., 10, 4315(2019).

[3] X. Huang et al. Fast, long-term, super-resolution imaging with Hessian structured illumination microscopy. Nat. Biotechnol., 36, 451(2018).

[4] R. Heintzmann, T. Huser. Super-resolution structured illumination microscopy. Chem. Rev., 117, 13890(2017).

[5] E. Moen et al. Deep learning for cellular image analysis. Nat. Methods, 16, 1233(2019).

[6] C. Belthangady, L. A. Royer. Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction. Nat. Methods, 16, 1215(2019).

[7] L. von Chamier, R. F. Laine, R. Henriques. Artificial intelligence for microscopy: what you should know. Biochem. Soc. Trans., 47, 1029(2019).

[8] M. G. L. Gustafsson et al. Three-dimensional resolution doubling in wide-field fluorescence microscopy by structured illumination. Biophys. J., 94, 4957(2008).

[9] Z. Wang et al. Rapid, artifact-reduced, image reconstruction for super-resolution structured illumination microscopy. Innovation, 4, 100425(2023).

[10] Z. Wang et al. High-speed image reconstruction for optically sectioned, super-resolution structured illumination microscopy. Adv. Photonics, 4, 026003(2022).

[11] V. Mannam et al. Real-time image denoising of mixed Poisson–Gaussian noise in fluorescence microscopy images using ImageJ. Optica, 9, 335(2022).

[12] X. Li et al. Real-time denoising enables high-sensitivity fluorescence time-lapse imaging beyond the shot-noise limit. Nat. Biotechnol., 41, 282(2023).

[13] C. N. Christensen et al. ML-SIM: universal reconstruction of structured illumination microscopy images using transfer learning. Biomed. Opt. Express, 12, 2720(2021).

[14] O. Ronneberger, P. Fischer, T. Brox. U-Net: convolutional networks for biomedical image segmentation(2015).

[15] L. Jin et al. Deep learning enables structured illumination microscopy with low light levels. Nat. Commun., 11, 1934(2020).

[16] T. Falk et al. U-Net: deep learning for cell counting, detection, and morphometry. Nat. Methods, 16, 67(2019).

[17] F. Xu et al. Real-time reconstruction using electro-optics modulator-based structured illumination microscopy. Opt. Express, 30, 13238(2022).

[18] D. Hendrycks, K. Gimpel. Gaussian error linear units (GELUs)(2023).

[19] J. Heaton. Ian Goodfellow, Yoshua Bengio, and Aaron Courville: deep learning. Genet. Program. Evol. Mach., 19, 305(2018).

[20] C. Qiao et al. Evaluation and development of deep neural networks for image super-resolution in optical microscopy. Nat. Methods, 18, 194(2021).

[21] D. P. Kingma, J. Ba. Adam: a method for stochastic optimization(2017).

[22] S. R. Sternberg. Biomedical image processing. Computer, 16, 22(1983).

[23] P. Kner et al. Super-resolution video microscopy of live cells by structured illumination. Nat. Methods, 6, 339(2009).

[24] L. Shao et al. Super-resolution 3D microscopy of live whole cells using structured illumination. Nat. Methods, 8, 1044(2011).

[25] Y. Chen et al. Widefield and total internal reflection fluorescent structured illumination microscopy with scanning galvo mirrors. J. Biomed. Opt., 23, 1(2018).

[26] Y. Chen et al. Multi-color live-cell super-resolution volume imaging with multi-angle interference microscopy. Nat. Commun., 9, 4818(2018).

[27] G. Wen et al. High-fidelity structured illumination microscopy by point-spread-function engineering. Light Sci. Appl., 10, 70(2021).

[28] R. Cao et al. Inverse matrix based phase estimation algorithm for structured illumination microscopy. Biomed. Opt. Express, 9, 5037(2018).

[29] M. Abadi et al. TensorFlow: large-scale machine learning on heterogeneous distributed systems(2016).

[30] J. Yang, J. Wright, L. Yu. Image super-resolution via sparse representation. IEEE Trans. Image Process., 19, 2861(2010).

[31] W. Zhao et al. Sparse deconvolution improves the resolution of live-cell super-resolution fluorescence microscopy. Nat. Biotechnol., 40, 606(2022).

[32] C. Qiao et al. Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processes. Nat. Biotechnol., 41, 367(2023).

[33] Y. He et al. Untrained neural network enhances the resolution of structured illumination microscopy under strong background and noise levels. Adv. Photonics Nexus, 2, 046005(2023).

[34] https://github.com/WenjieLab/Deep-learning-based-real-time-SIM

Tools

Get Citation

Copy Citation Text

Hanchu Ye, Zitong Ye, Yunbo Chen, Jinfeng Zhang, Xu Liu, Cuifang Kuang, Youhua Chen, Wenjie Liu. Video-level and high-fidelity super-resolution SIM reconstruction enabled by deep learning[J]. Advanced Imaging, 2024, 1(1): 011001

Download Citation

EndNote(RIS)BibTex Plain Text

Set citation alerts for article

Save article for my favorites

Paper Information

Category: Research Article

Received: Dec. 8, 2023

Accepted: Feb. 6, 2024

Published Online: Apr. 7, 2024

The Author Email: Kuang Cuifang (cfkuang@zju.edu.cn), Chen Youhua (chenyh21012@zju.edu.cn), Liu Wenjie (wenjieliu@zju.edu.cn)

DOI:10.3788/AI.2024.10003

Topics

laser devices and laser physics

Lasers and Laser Optics

laser manufacturing

Instrumentation, Measurement and Metrology