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Forsgren, E., Edlund, C., Oliver, M., Barnes, K., Sjögren, R. & Jackson, T. R. (2022). High-throughput widefield fluorescence imaging of 3D samples using deep learning for 2D projection image restoration. PLOS ONE, 17(5 May), Article ID e0264241.
Open this publication in new window or tab >>High-throughput widefield fluorescence imaging of 3D samples using deep learning for 2D projection image restoration
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2022 (English)In: PLOS ONE, E-ISSN 1932-6203, Vol. 17, no 5 May, article id e0264241Article in journal (Refereed) Published
Abstract [en]

Fluorescence microscopy is a core method for visualizing and quantifying the spatial and temporal dynamics of complex biological processes. While many fluorescent microscopy techniques exist, due to its cost-effectiveness and accessibility, widefield fluorescent imaging remains one of the most widely used. To accomplish imaging of 3D samples, conventional widefield fluorescence imaging entails acquiring a sequence of 2D images spaced along the z-dimension, typically called a z-stack. Oftentimes, the first step in an analysis pipeline is to project that 3D volume into a single 2D image because 3D image data can be cumbersome to manage and challenging to analyze and interpret. Furthermore, z-stack acquisition is often time-consuming, which consequently may induce photodamage to the biological sample; these are major barriers for workflows that require high-throughput, such as drug screening. As an alternative to z-stacks, axial sweep acquisition schemes have been proposed to circumvent these drawbacks and offer potential of 100-fold faster image acquisition for 3D-samples compared to z-stack acquisition. Unfortunately, these acquisition techniques generate low-quality 2D z-projected images that require restoration with unwieldy, computationally heavy algorithms before the images can be interrogated. We propose a novel workflow to combine axial z-sweep acquisition with deep learning-based image restoration, ultimately enabling high-throughput and high-quality imaging of complex 3D-samples using 2D projection images. To demonstrate the capabilities of our proposed workflow, we apply it to live-cell imaging of large 3D tumor spheroid cultures and find we can produce high-fidelity images appropriate for quantitative analysis. Therefore, we conclude that combining axial z-sweep image acquisition with deep learning-based image restoration enables high-throughput and high-quality fluorescence imaging of complex 3D biological samples.

Place, publisher, year, edition, pages
Public Library of Science, 2022
National Category
Computer Vision and Robotics (Autonomous Systems)
urn:nbn:se:umu:diva-203153 (URN)10.1371/journal.pone.0264241 (DOI)001016382300005 ()35588399 (PubMedID)2-s2.0-85130357196 (Scopus ID)
eSSENCE - An eScience Collaboration
Available from: 2023-01-16 Created: 2023-01-16 Last updated: 2023-09-05Bibliographically approved

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