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Leukocyte differentiation in bronchoalveolar lavage fluids using higher harmonic generation microscopy and deep learning
BACKGROUND: In diseases such as interstitial lung diseases (ILDs), patient diagnosis relies on diagnostic analysis of bronchoalveolar lavage fluid (BALF) and biopsies. Immunological BALF analysis includes differentiation of leukocytes by standard cytological techniques that are labor-intensive and t...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10298778/ https://www.ncbi.nlm.nih.gov/pubmed/37368904 http://dx.doi.org/10.1371/journal.pone.0279525 |
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author | van Huizen, Laura M. G. Blokker, Max Rip, Yael Veta, Mitko Mooij Kalverda, Kirsten A. Bonta, Peter I. Duitman, Jan Willem Groot, Marie Louise |
author_facet | van Huizen, Laura M. G. Blokker, Max Rip, Yael Veta, Mitko Mooij Kalverda, Kirsten A. Bonta, Peter I. Duitman, Jan Willem Groot, Marie Louise |
author_sort | van Huizen, Laura M. G. |
collection | PubMed |
description | BACKGROUND: In diseases such as interstitial lung diseases (ILDs), patient diagnosis relies on diagnostic analysis of bronchoalveolar lavage fluid (BALF) and biopsies. Immunological BALF analysis includes differentiation of leukocytes by standard cytological techniques that are labor-intensive and time-consuming. Studies have shown promising leukocyte identification performance on blood fractions, using third harmonic generation (THG) and multiphoton excited autofluorescence (MPEF) microscopy. OBJECTIVE: To extend leukocyte differentiation to BALF samples using THG/MPEF microscopy, and to show the potential of a trained deep learning algorithm for automated leukocyte identification and quantification. METHODS: Leukocytes from blood obtained from three healthy individuals and one asthma patient, and BALF samples from six ILD patients were isolated and imaged using label-free microscopy. The cytological characteristics of leukocytes, including neutrophils, eosinophils, lymphocytes, and macrophages, in terms of cellular and nuclear morphology, and THG and MPEF signal intensity, were determined. A deep learning model was trained on 2D images and used to estimate the leukocyte ratios at the image-level using the differential cell counts obtained using standard cytological techniques as reference. RESULTS: Different leukocyte populations were identified in BALF samples using label-free microscopy, showing distinctive cytological characteristics. Based on the THG/MPEF images, the deep learning network has learned to identify individual cells and was able to provide a reasonable estimate of the leukocyte percentage, reaching >90% accuracy on BALF samples in the hold-out testing set. CONCLUSIONS: Label-free THG/MPEF microscopy in combination with deep learning is a promising technique for instant differentiation and quantification of leukocytes. Immediate feedback on leukocyte ratios has potential to speed-up the diagnostic process and to reduce costs, workload and inter-observer variations. |
format | Online Article Text |
id | pubmed-10298778 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-102987782023-06-28 Leukocyte differentiation in bronchoalveolar lavage fluids using higher harmonic generation microscopy and deep learning van Huizen, Laura M. G. Blokker, Max Rip, Yael Veta, Mitko Mooij Kalverda, Kirsten A. Bonta, Peter I. Duitman, Jan Willem Groot, Marie Louise PLoS One Research Article BACKGROUND: In diseases such as interstitial lung diseases (ILDs), patient diagnosis relies on diagnostic analysis of bronchoalveolar lavage fluid (BALF) and biopsies. Immunological BALF analysis includes differentiation of leukocytes by standard cytological techniques that are labor-intensive and time-consuming. Studies have shown promising leukocyte identification performance on blood fractions, using third harmonic generation (THG) and multiphoton excited autofluorescence (MPEF) microscopy. OBJECTIVE: To extend leukocyte differentiation to BALF samples using THG/MPEF microscopy, and to show the potential of a trained deep learning algorithm for automated leukocyte identification and quantification. METHODS: Leukocytes from blood obtained from three healthy individuals and one asthma patient, and BALF samples from six ILD patients were isolated and imaged using label-free microscopy. The cytological characteristics of leukocytes, including neutrophils, eosinophils, lymphocytes, and macrophages, in terms of cellular and nuclear morphology, and THG and MPEF signal intensity, were determined. A deep learning model was trained on 2D images and used to estimate the leukocyte ratios at the image-level using the differential cell counts obtained using standard cytological techniques as reference. RESULTS: Different leukocyte populations were identified in BALF samples using label-free microscopy, showing distinctive cytological characteristics. Based on the THG/MPEF images, the deep learning network has learned to identify individual cells and was able to provide a reasonable estimate of the leukocyte percentage, reaching >90% accuracy on BALF samples in the hold-out testing set. CONCLUSIONS: Label-free THG/MPEF microscopy in combination with deep learning is a promising technique for instant differentiation and quantification of leukocytes. Immediate feedback on leukocyte ratios has potential to speed-up the diagnostic process and to reduce costs, workload and inter-observer variations. Public Library of Science 2023-06-27 /pmc/articles/PMC10298778/ /pubmed/37368904 http://dx.doi.org/10.1371/journal.pone.0279525 Text en © 2023 van Huizen et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article van Huizen, Laura M. G. Blokker, Max Rip, Yael Veta, Mitko Mooij Kalverda, Kirsten A. Bonta, Peter I. Duitman, Jan Willem Groot, Marie Louise Leukocyte differentiation in bronchoalveolar lavage fluids using higher harmonic generation microscopy and deep learning |
title | Leukocyte differentiation in bronchoalveolar lavage fluids using higher harmonic generation microscopy and deep learning |
title_full | Leukocyte differentiation in bronchoalveolar lavage fluids using higher harmonic generation microscopy and deep learning |
title_fullStr | Leukocyte differentiation in bronchoalveolar lavage fluids using higher harmonic generation microscopy and deep learning |
title_full_unstemmed | Leukocyte differentiation in bronchoalveolar lavage fluids using higher harmonic generation microscopy and deep learning |
title_short | Leukocyte differentiation in bronchoalveolar lavage fluids using higher harmonic generation microscopy and deep learning |
title_sort | leukocyte differentiation in bronchoalveolar lavage fluids using higher harmonic generation microscopy and deep learning |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10298778/ https://www.ncbi.nlm.nih.gov/pubmed/37368904 http://dx.doi.org/10.1371/journal.pone.0279525 |
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