Deep learning enhanced light sheet fluorescence microscopy for in vivo 4D imaging of zebrafish heart beating

Meng Zhang; Renjian Li; Songnian Fu; Sunil Kumar; James Mcginty; Yuwen Qin; Lingling Chen

doi:10.1038/s41377-024-01710-z

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Deep learning enhanced light sheet fluorescence microscopy for in vivo 4D imaging of zebrafish heart beating

Original Articles

- Deep learning enhanced light sheet fluorescence microscopy for in vivo 4D imaging of zebrafish heart beating
- Light: Science & Applications Vol. 14, Issue 4, Pages: 970-983(2025)
- Affiliations：
  
  1.School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
  2.College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
  3.Institute of Advanced Photonics Technology, School of Information Engineering, Guangdong University of Technology, Guangzhou 51006, China
  4.Photonics Group, Department of Physics, Imperial College London, London SW7 2AZ, UK
- Author bio：
  
  Yuwen Qin (qinyw@gdut.edu.cn)
  Lingling Chen (chenlingling@sztu.edu.cn)
- Funds：
- DOI：10.1038/s41377-024-01710-z
  CLC：
- Received：29 May 2024，
  
  Revised：09 October 2024，
  
  Accepted：2024-12-02，
  
  Published Online：25 February 2025，
  
  Published：30 April 2025
- Accepted：
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Zhang, M. et al. Deep learning enhanced light sheet fluorescence microscopy for in vivo 4D imaging of zebrafish heart beating. Light: Science & Applications, 14, 970-983 (2025).
DOI：

Zhang, M. et al. Deep learning enhanced light sheet fluorescence microscopy for in vivo 4D imaging of zebrafish heart beating. Light: Science & Applications, 14, 970-983 (2025). DOI： 10.1038/s41377-024-01710-z.

摘要

Abstract

Time-resolved volumetric fluorescence imaging over an extended duration with high spatial/temporal resolution is a key driving force in biomedical research for investigating spatial-temporal dynamics at organism-level systems

yet it remains a major challenge due to the trade-off among imaging speed

light exposure

illumination power

and image quality. Here

we present a deep-learning enhanced light sheet fluorescence microscopy (LSFM) approach that addresses the restoration of rapid volumetric time-lapse imaging with less than 0.03% light exposure and 3.3% acquisition time compared to a typical standard acquisition. We demonstrate that the convolutional neural network (CNN)-transformer network developed here

namely U-net integrated transformer (UI-Trans)

successfully achieves the mitigation of complex noise-scattering-coupled degradation and outperforms state-of-the-art deep learning networks

due to its capability of faithfully learning fine details while comprehending complex global features. With the fast generation of appropriate training data via flexible switching between confocal line-scanning LSFM (LS-LSFM) and conventional LSFM

this method achieves a three- to five-fold signal-to-noise ratio (SNR) improvement and ~1.8 times contrast improvement in ex vivo zebrafish heart imaging and long-term in vivo 4D (3D morphology + time) imaging of heartbeat dynamics at different developmental stages with ultra-economical acquisitions in terms of light dosage and acquisition time.

关键词

Keywords

references

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