Label2label: training a neural network to selectively restore cellular structures in fluorescence microscopy

Immunofluorescence microscopy is routinely used to visualise the spatial distribution of proteins that dictates their cellular function. However, unspecific antibody binding often results in high cytosolic background signals, decreasing the image contrast of a target structure. Recently, convolution...

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Published inJournal of cell science Vol. 135; no. 3
Main Authors Kölln, Lisa Sophie, Salem, Omar, Valli, Jessica, Hansen, Carsten Gram, McConnell, Gail
Format Journal Article
LanguageEnglish
Published England The Company of Biologists Ltd 01.02.2022
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Abstract Immunofluorescence microscopy is routinely used to visualise the spatial distribution of proteins that dictates their cellular function. However, unspecific antibody binding often results in high cytosolic background signals, decreasing the image contrast of a target structure. Recently, convolutional neural networks (CNNs) were successfully employed for image restoration in immunofluorescence microscopy, but current methods cannot correct for those background signals. We report a new method that trains a CNN to reduce unspecific signals in immunofluorescence images; we name this method label2label (L2L). In L2L, a CNN is trained with image pairs of two non-identical labels that target the same cellular structure. We show that after L2L training a network predicts images with significantly increased contrast of a target structure, which is further improved after implementing a multiscale structural similarity loss function. Here, our results suggest that sample differences in the training data decrease hallucination effects that are observed with other methods. We further assess the performance of a cycle generative adversarial network, and show that a CNN can be trained to separate structures in superposed immunofluorescence images of two targets.
AbstractList Immunofluorescence microscopy is routinely used to visualise the spatial distribution of proteins that dictates their cellular function. However, unspecific antibody binding often results in high cytosolic background signals, decreasing the image contrast of a target structure. Recently, convolutional neural networks (CNNs) were successfully employed for image restoration in immunofluorescence microscopy, but current methods cannot correct for those background signals. We report a new method that trains a CNN to reduce unspecific signals in immunofluorescence images; we name this method label2label (L2L). In L2L, a CNN is trained with image pairs of two non-identical labels that target the same cellular structure. We show that after L2L training a network predicts images with significantly increased contrast of a target structure, which is further improved after implementing a multiscale structural similarity loss function. Here, our results suggest that sample differences in the training data decrease hallucination effects that are observed with other methods. We further assess the performance of a cycle generative adversarial network, and show that a CNN can be trained to separate structures in superposed immunofluorescence images of two targets.
Immunofluorescence microscopy is routinely used to visualise the spatial distribution of proteins that dictates their cellular function. However, unspecific antibody binding often results in high cytosolic background signals, decreasing the image contrast of a target structure. Recently, convolutional neural networks (CNNs) were successfully employed for image restoration in immunofluorescence microscopy, but current methods cannot correct for those background signals. We report a new method that trains a CNN to reduce unspecific signals in immunofluorescence images; we name this method label2label (L2L). In L2L, a CNN is trained with image pairs of two non-identical labels that target the same cellular structure. We show that after L2L training a network predicts images with significantly increased contrast of a target structure, which is further improved after implementing a multiscale structural similarity loss function. Here, our results suggest that sample differences in the training data decrease hallucination effects that are observed with other methods. We further assess the performance of a cycle generative adversarial network, and show that a CNN can be trained to separate structures in superposed immunofluorescence images of two targets. Summary: Label2label is a new deep learning-based image restoration method that reduces cytosolic background signals in immunofluorescence images of cellular structures.
Author Hansen, Carsten Gram
Salem, Omar
Kölln, Lisa Sophie
McConnell, Gail
Valli, Jessica
AuthorAffiliation 2 University of Edinburgh, Centre for Inflammation Research , Edinburgh EH16 4TJ , UK
1 University of Strathclyde , Department of Physics, Glasgow G4 0NG , UK
4 Edinburgh Super Resolution Imaging Consortium , Institute of Biological Chemistry, Biophysics and Bioengineering, Heriot-Watt University , Edinburgh EH14 4AS , UK
3 University of Edinburgh, Institute for Regeneration and Repair , Edinburgh EH16 4UU , UK
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Issue 3
Keywords Content-aware image restoration
Noise2noise
Antibody labelling
Convolutional neural networks
Fluorescence microscopy
Cellular structures
Language English
License 2022. Published by The Company of Biologists Ltd.
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Snippet Immunofluorescence microscopy is routinely used to visualise the spatial distribution of proteins that dictates their cellular function. However, unspecific...
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SubjectTerms Cellular Structures
Image Processing, Computer-Assisted - methods
Imaging
Microscopy, Fluorescence
Neural Networks, Computer
Tools in Cell Biology
Title Label2label: training a neural network to selectively restore cellular structures in fluorescence microscopy
URI https://www.ncbi.nlm.nih.gov/pubmed/35022745
https://search.proquest.com/docview/2619540919
https://pubmed.ncbi.nlm.nih.gov/PMC8918818
Volume 135
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