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Machine and deep learning single-cell segmentation and quantification of multi-dimensional tissue images

Eliot T McKinley, Joseph T Roland, Jeffrey L Franklin, Mary Catherine Macedonia, Paige N Vega, Susie Shin, Robert J Coffey, View ORCID ProfileKen S Lau
doi: https://doi.org/10.1101/790162
Eliot T McKinley
Vanderbilt University and Vanderbilt University Medical Center, Nashville, TN, USA
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  • For correspondence: eliot.t.mckinley.1@vanderbilt.edu
Joseph T Roland
Vanderbilt University and Vanderbilt University Medical Center, Nashville, TN, USA
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Jeffrey L Franklin
Vanderbilt University and Vanderbilt University Medical Center, Nashville, TN, USA
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Mary Catherine Macedonia
Vanderbilt University and Vanderbilt University Medical Center, Nashville, TN, USA
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Paige N Vega
Vanderbilt University and Vanderbilt University Medical Center, Nashville, TN, USA
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Susie Shin
Vanderbilt University and Vanderbilt University Medical Center, Nashville, TN, USA
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Robert J Coffey
Vanderbilt University and Vanderbilt University Medical Center, Nashville, TN, USA
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Ken S Lau
Vanderbilt University and Vanderbilt University Medical Center, Nashville, TN, USA
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  • ORCID record for Ken S Lau
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Abstract

Increasingly, highly multiplexed in situ tissue imaging methods are used to profile protein expression at the single-cell level. However, a critical limitation is a lack of robust cell segmentation tools applicable for sections of tissues with a complex architecture and multiple cell types. Using human colorectal adenomas, we present a pipeline for cell segmentation and quantification that utilizes machine learning-based pixel classification to define cellular compartments, a novel method for extending incomplete cell membranes, quantification of antibody staining, and a deep learning-based cell shape descriptor. We envision that this method can be broadly applied to different imaging platforms and tissue types.

Footnotes

  • https://github.com/Coffey-Lab/CellSegmentation

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted October 02, 2019.
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Machine and deep learning single-cell segmentation and quantification of multi-dimensional tissue images
Eliot T McKinley, Joseph T Roland, Jeffrey L Franklin, Mary Catherine Macedonia, Paige N Vega, Susie Shin, Robert J Coffey, Ken S Lau
bioRxiv 790162; doi: https://doi.org/10.1101/790162
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Machine and deep learning single-cell segmentation and quantification of multi-dimensional tissue images
Eliot T McKinley, Joseph T Roland, Jeffrey L Franklin, Mary Catherine Macedonia, Paige N Vega, Susie Shin, Robert J Coffey, Ken S Lau
bioRxiv 790162; doi: https://doi.org/10.1101/790162

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