Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy

Min Guo (), Yicong Wu, Chad M. Hobson, Yijun Su, Shuhao Qian, Eric Krueger, Ryan Christensen, Grant Kroeschell, Johnny Bui, Matthew Chaw, Lixia Zhang, Jiamin Liu, Xuekai Hou, Xiaofei Han, Zhiye Lu, Xuefei Ma, Alexander Zhovmer, Christian Combs, Mark Moyle, Eviatar Yemini, Huafeng Liu, Zhiyi Liu, Alexandre Benedetto, Patrick Riviere, Daniel Colón-Ramos and Hari Shroff
Additional contact information
Min Guo: Zhejiang University
Yicong Wu: National Institutes of Health
Chad M. Hobson: Howard Hughes Medical Institute (HHMI)
Yijun Su: National Institutes of Health
Shuhao Qian: Zhejiang University
Eric Krueger: National Institutes of Health
Ryan Christensen: National Institutes of Health
Grant Kroeschell: National Institutes of Health
Johnny Bui: National Institutes of Health
Matthew Chaw: National Institutes of Health
Lixia Zhang: National Institutes of Health
Jiamin Liu: National Institutes of Health
Xuekai Hou: Zhejiang University
Xiaofei Han: National Institutes of Health
Zhiye Lu: National Institutes of Health
Xuefei Ma: National Institutes of Health
Alexander Zhovmer: U.S. Food and Drug Administration
Christian Combs: National Institutes of Health
Mark Moyle: Brigham Young University-Idaho
Eviatar Yemini: UMass Chan Medical School
Huafeng Liu: Zhejiang University
Zhiyi Liu: Zhejiang University
Alexandre Benedetto: Lancaster University
Patrick Riviere: University of Chicago
Daniel Colón-Ramos: Marine Biological Laboratory
Hari Shroff: National Institutes of Health

Nature Communications, 2025, vol. 16, issue 1, 1-19

Abstract: Abstract Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations. We use simulations and experiments to show that applying the trained ‘de-aberration’ networks outperforms alternative methods, providing restoration on par with adaptive optics techniques; and subsequently apply the networks to diverse datasets captured with confocal, light-sheet, multi-photon, and super-resolution microscopy. In all cases, the improved quality of the restored data facilitates qualitative image inspection and improves downstream image quantitation, including orientational analysis of blood vessels in mouse tissue and improved membrane and nuclear segmentation in C. elegans embryos.

Date: 2025
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DOI: 10.1038/s41467-024-55267-x

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