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  • 1.
    Forsgren, Edvin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Deep Learning to Enhance Fluorescent Signals in Live Cell Imaging2020Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
    Fulltekst (pdf)
    fulltext
  • 2.
    Forsgren, Edvin
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Edlund, Christoffer
    Sartorius Corporate Research, Sartorius Stedim Data Analytics AB, Umeå, Sweden.
    Oliver, Miniver
    Sartorius BioAnalytics, Essen BioScience, Ltd., Units 2 & 3 The Quadrant, Hertfordshire, Royston, United Kingdom.
    Barnes, Kalpana
    Sartorius BioAnalytics, Essen BioScience, Ltd., Units 2 & 3 The Quadrant, Hertfordshire, Royston, United Kingdom.
    Sjögren, Rickard
    Sartorius Corporate Research, Sartorius Stedim Data Analytics AB, Umeå, Sweden.
    Jackson, Timothy R.
    Sartorius BioAnalytics, Essen BioScience, Ltd., Units 2 & 3 The Quadrant, Hertfordshire, Royston, United Kingdom.
    High-throughput widefield fluorescence imaging of 3D samples using deep learning for 2D projection image restoration2022Inngår i: PLOS ONE, E-ISSN 1932-6203, Vol. 17, nr 5 May, artikkel-id e0264241Artikkel i tidsskrift (Fagfellevurdert)
    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.

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