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Quantifying the effects of biopsy fixation and staining panel design on automatic instance segmentation of immune cells in human lupus nephritis

Significance: Lupus nephritis (LuN) is a chronic inflammatory kidney disease. The cellular mechanisms by which LuN progresses to kidney failure are poorly characterized. Automated instance segmentation of immune cells in immunofluorescence images of LuN can probe these cellular interactions. Aim: Ou...

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Autores principales: Durkee, Madeleine S., Abraham, Rebecca, Ai, Junting, Veselits, Margaret, Clark, Marcus R., Giger, Maryellen L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Society of Photo-Optical Instrumentation Engineers 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7791891/
https://www.ncbi.nlm.nih.gov/pubmed/33420765
http://dx.doi.org/10.1117/1.JBO.26.2.022910
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author Durkee, Madeleine S.
Abraham, Rebecca
Ai, Junting
Veselits, Margaret
Clark, Marcus R.
Giger, Maryellen L.
author_facet Durkee, Madeleine S.
Abraham, Rebecca
Ai, Junting
Veselits, Margaret
Clark, Marcus R.
Giger, Maryellen L.
author_sort Durkee, Madeleine S.
collection PubMed
description Significance: Lupus nephritis (LuN) is a chronic inflammatory kidney disease. The cellular mechanisms by which LuN progresses to kidney failure are poorly characterized. Automated instance segmentation of immune cells in immunofluorescence images of LuN can probe these cellular interactions. Aim: Our specific goal is to quantify how sample fixation and staining panel design impact automated instance segmentation and characterization of immune cells. Approach: Convolutional neural networks (CNNs) were trained to segment immune cells in fluorescence confocal images of LuN biopsies. Three datasets were used to probe the effects of fixation methods on cell features and the effects of one-marker versus two-marker per cell staining panels on CNN performance. Results: Networks trained for multi-class instance segmentation on fresh-frozen and formalin-fixed, paraffin-embedded (FFPE) samples stained with a two-marker panel had sensitivities of 0.87 and 0.91 and specificities of 0.82 and 0.88, respectively. Training on samples with a one-marker panel reduced sensitivity (0.72). Cell size and intercellular distances were significantly smaller in FFPE samples compared to fresh frozen (Kolmogorov–Smirnov, [Formula: see text]). Conclusions: Fixation method significantly reduces cell size and intercellular distances in LuN biopsies. The use of two markers to identify cell subsets showed improved CNN sensitivity relative to using a single marker.
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spelling pubmed-77918912021-01-12 Quantifying the effects of biopsy fixation and staining panel design on automatic instance segmentation of immune cells in human lupus nephritis Durkee, Madeleine S. Abraham, Rebecca Ai, Junting Veselits, Margaret Clark, Marcus R. Giger, Maryellen L. J Biomed Opt Special Series on Artificial Intelligence and Machine Learning in Biomedical Optics Significance: Lupus nephritis (LuN) is a chronic inflammatory kidney disease. The cellular mechanisms by which LuN progresses to kidney failure are poorly characterized. Automated instance segmentation of immune cells in immunofluorescence images of LuN can probe these cellular interactions. Aim: Our specific goal is to quantify how sample fixation and staining panel design impact automated instance segmentation and characterization of immune cells. Approach: Convolutional neural networks (CNNs) were trained to segment immune cells in fluorescence confocal images of LuN biopsies. Three datasets were used to probe the effects of fixation methods on cell features and the effects of one-marker versus two-marker per cell staining panels on CNN performance. Results: Networks trained for multi-class instance segmentation on fresh-frozen and formalin-fixed, paraffin-embedded (FFPE) samples stained with a two-marker panel had sensitivities of 0.87 and 0.91 and specificities of 0.82 and 0.88, respectively. Training on samples with a one-marker panel reduced sensitivity (0.72). Cell size and intercellular distances were significantly smaller in FFPE samples compared to fresh frozen (Kolmogorov–Smirnov, [Formula: see text]). Conclusions: Fixation method significantly reduces cell size and intercellular distances in LuN biopsies. The use of two markers to identify cell subsets showed improved CNN sensitivity relative to using a single marker. Society of Photo-Optical Instrumentation Engineers 2021-01-08 2021-02 /pmc/articles/PMC7791891/ /pubmed/33420765 http://dx.doi.org/10.1117/1.JBO.26.2.022910 Text en © 2021 The Authors https://creativecommons.org/licenses/by/4.0/ Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
spellingShingle Special Series on Artificial Intelligence and Machine Learning in Biomedical Optics
Durkee, Madeleine S.
Abraham, Rebecca
Ai, Junting
Veselits, Margaret
Clark, Marcus R.
Giger, Maryellen L.
Quantifying the effects of biopsy fixation and staining panel design on automatic instance segmentation of immune cells in human lupus nephritis
title Quantifying the effects of biopsy fixation and staining panel design on automatic instance segmentation of immune cells in human lupus nephritis
title_full Quantifying the effects of biopsy fixation and staining panel design on automatic instance segmentation of immune cells in human lupus nephritis
title_fullStr Quantifying the effects of biopsy fixation and staining panel design on automatic instance segmentation of immune cells in human lupus nephritis
title_full_unstemmed Quantifying the effects of biopsy fixation and staining panel design on automatic instance segmentation of immune cells in human lupus nephritis
title_short Quantifying the effects of biopsy fixation and staining panel design on automatic instance segmentation of immune cells in human lupus nephritis
title_sort quantifying the effects of biopsy fixation and staining panel design on automatic instance segmentation of immune cells in human lupus nephritis
topic Special Series on Artificial Intelligence and Machine Learning in Biomedical Optics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7791891/
https://www.ncbi.nlm.nih.gov/pubmed/33420765
http://dx.doi.org/10.1117/1.JBO.26.2.022910
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