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Hardware-software co-design of an open-source automatic multimodal whole slide histopathology imaging system

SIGNIFICANCE: Advanced digital control of microscopes and programmable data acquisition workflows have become increasingly important for improving the throughput and reproducibility of optical imaging experiments. Combinations of imaging modalities have enabled a more comprehensive understanding of...

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Autores principales: Li, Bin, Nelson, Michael S., Chacko, Jenu V., Cudworth, Nathan, Eliceiri, Kevin W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Society of Photo-Optical Instrumentation Engineers 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9905038/
https://www.ncbi.nlm.nih.gov/pubmed/36761254
http://dx.doi.org/10.1117/1.JBO.28.2.026501
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author Li, Bin
Nelson, Michael S.
Chacko, Jenu V.
Cudworth, Nathan
Eliceiri, Kevin W.
author_facet Li, Bin
Nelson, Michael S.
Chacko, Jenu V.
Cudworth, Nathan
Eliceiri, Kevin W.
author_sort Li, Bin
collection PubMed
description SIGNIFICANCE: Advanced digital control of microscopes and programmable data acquisition workflows have become increasingly important for improving the throughput and reproducibility of optical imaging experiments. Combinations of imaging modalities have enabled a more comprehensive understanding of tissue biology and tumor microenvironments in histopathological studies. However, insufficient imaging throughput and complicated workflows still limit the scalability of multimodal histopathology imaging. AIM: We present a hardware-software co-design of a whole slide scanning system for high-throughput multimodal tissue imaging, including brightfield (BF) and laser scanning microscopy. APPROACH: The system can automatically detect regions of interest using deep neural networks in a low-magnification rapid BF scan of the tissue slide and then conduct high-resolution BF scanning and laser scanning imaging on targeted regions with deep learning-based run-time denoising and resolution enhancement. The acquisition workflow is built using Pycro-Manager, a Python package that bridges hardware control libraries of the Java-based open-source microscopy software Micro-Manager in a Python environment. RESULTS: The system can achieve optimized imaging settings for both modalities with minimized human intervention and speed up the laser scanning by an order of magnitude with run-time image processing. CONCLUSIONS: The system integrates the acquisition pipeline and data analysis pipeline into a single workflow that improves the throughput and reproducibility of multimodal histopathological imaging.
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spelling pubmed-99050382023-02-08 Hardware-software co-design of an open-source automatic multimodal whole slide histopathology imaging system Li, Bin Nelson, Michael S. Chacko, Jenu V. Cudworth, Nathan Eliceiri, Kevin W. J Biomed Opt Microscopy SIGNIFICANCE: Advanced digital control of microscopes and programmable data acquisition workflows have become increasingly important for improving the throughput and reproducibility of optical imaging experiments. Combinations of imaging modalities have enabled a more comprehensive understanding of tissue biology and tumor microenvironments in histopathological studies. However, insufficient imaging throughput and complicated workflows still limit the scalability of multimodal histopathology imaging. AIM: We present a hardware-software co-design of a whole slide scanning system for high-throughput multimodal tissue imaging, including brightfield (BF) and laser scanning microscopy. APPROACH: The system can automatically detect regions of interest using deep neural networks in a low-magnification rapid BF scan of the tissue slide and then conduct high-resolution BF scanning and laser scanning imaging on targeted regions with deep learning-based run-time denoising and resolution enhancement. The acquisition workflow is built using Pycro-Manager, a Python package that bridges hardware control libraries of the Java-based open-source microscopy software Micro-Manager in a Python environment. RESULTS: The system can achieve optimized imaging settings for both modalities with minimized human intervention and speed up the laser scanning by an order of magnitude with run-time image processing. CONCLUSIONS: The system integrates the acquisition pipeline and data analysis pipeline into a single workflow that improves the throughput and reproducibility of multimodal histopathological imaging. Society of Photo-Optical Instrumentation Engineers 2023-02-08 2023-02 /pmc/articles/PMC9905038/ /pubmed/36761254 http://dx.doi.org/10.1117/1.JBO.28.2.026501 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
spellingShingle Microscopy
Li, Bin
Nelson, Michael S.
Chacko, Jenu V.
Cudworth, Nathan
Eliceiri, Kevin W.
Hardware-software co-design of an open-source automatic multimodal whole slide histopathology imaging system
title Hardware-software co-design of an open-source automatic multimodal whole slide histopathology imaging system
title_full Hardware-software co-design of an open-source automatic multimodal whole slide histopathology imaging system
title_fullStr Hardware-software co-design of an open-source automatic multimodal whole slide histopathology imaging system
title_full_unstemmed Hardware-software co-design of an open-source automatic multimodal whole slide histopathology imaging system
title_short Hardware-software co-design of an open-source automatic multimodal whole slide histopathology imaging system
title_sort hardware-software co-design of an open-source automatic multimodal whole slide histopathology imaging system
topic Microscopy
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9905038/
https://www.ncbi.nlm.nih.gov/pubmed/36761254
http://dx.doi.org/10.1117/1.JBO.28.2.026501
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