Deep learning enhanced fluorescence fluctuations super-resolution microscopy

Yanru Li; Lixin Liu; Lin Wang

doi:10.1117/12.3039254

8 October 2024 Deep learning enhanced fluorescence fluctuations super-resolution microscopy

Yanru Li, Lixin Liu, Lin Wang

Proceedings Volume 13271, Third Conference on Biomedical Photonics and Cross-Fusion (BPC 2024); 1327108 (2024) https://doi.org/10.1117/12.3039254
Event: Third Conference on Biomedical Photonics and Cross-Fusion (BPC 2024), 2024, Shanghai, China

Abstract

Fluorescence fluctuations super-resolution microscopy (FF-SRM) is a powerful tool in imaging and monitoring of biological subcellular structures and dynamics in cells. A variety of image reconstruction algorithms have been developed for FF-SRM. In order to obtain high spatiotemporal resolution, a U-Net-based deep learning method for super-resolution imaging of fluorescence fluctuations was developed. With just 20 frames, super-resolution images reconstructed using U-Net model could be comparable to those reconstructed using VeSRRF algorithm with several hundred frames, demonstrating its capability of advancing imaging capabilities.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yanru Li, Lixin Liu, and Lin Wang "Deep learning enhanced fluorescence fluctuations super-resolution microscopy", Proc. SPIE 13271, Third Conference on Biomedical Photonics and Cross-Fusion (BPC 2024), 1327108 (8 October 2024); https://doi.org/10.1117/12.3039254

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