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Dice-XMBD: Deep Learning-Based Cell Segmentation for Imaging Mass Cytometry

Highly multiplexed imaging technology is a powerful tool to facilitate understanding the composition and interactions of cells in tumor microenvironments at subcellular resolution, which is crucial for both basic research and clinical applications. Imaging mass cytometry (IMC), a multiplex imaging m...

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Detalles Bibliográficos
Autores principales: Xiao, Xu, Qiao, Ying, Jiao, Yudi, Fu, Na, Yang, Wenxian, Wang, Liansheng, Yu, Rongshan, Han, Jiahuai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8480472/
https://www.ncbi.nlm.nih.gov/pubmed/34603385
http://dx.doi.org/10.3389/fgene.2021.721229
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author Xiao, Xu
Qiao, Ying
Jiao, Yudi
Fu, Na
Yang, Wenxian
Wang, Liansheng
Yu, Rongshan
Han, Jiahuai
author_facet Xiao, Xu
Qiao, Ying
Jiao, Yudi
Fu, Na
Yang, Wenxian
Wang, Liansheng
Yu, Rongshan
Han, Jiahuai
author_sort Xiao, Xu
collection PubMed
description Highly multiplexed imaging technology is a powerful tool to facilitate understanding the composition and interactions of cells in tumor microenvironments at subcellular resolution, which is crucial for both basic research and clinical applications. Imaging mass cytometry (IMC), a multiplex imaging method recently introduced, can measure up to 100 markers simultaneously in one tissue section by using a high-resolution laser with a mass cytometer. However, due to its high resolution and large number of channels, how to process and interpret the image data from IMC remains a key challenge to its further applications. Accurate and reliable single cell segmentation is the first and a critical step to process IMC image data. Unfortunately, existing segmentation pipelines either produce inaccurate cell segmentation results or require manual annotation, which is very time consuming. Here, we developed Dice-XMBD, a Deep learnIng-based Cell sEgmentation algorithm for tissue multiplexed imaging data. In comparison with other state-of-the-art cell segmentation methods currently used for IMC images, Dice-XMBD generates more accurate single cell masks efficiently on IMC images produced with different nuclear, membrane, and cytoplasm markers. All codes and datasets are available at https://github.com/xmuyulab/Dice-XMBD.
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spelling pubmed-84804722021-09-30 Dice-XMBD: Deep Learning-Based Cell Segmentation for Imaging Mass Cytometry Xiao, Xu Qiao, Ying Jiao, Yudi Fu, Na Yang, Wenxian Wang, Liansheng Yu, Rongshan Han, Jiahuai Front Genet Genetics Highly multiplexed imaging technology is a powerful tool to facilitate understanding the composition and interactions of cells in tumor microenvironments at subcellular resolution, which is crucial for both basic research and clinical applications. Imaging mass cytometry (IMC), a multiplex imaging method recently introduced, can measure up to 100 markers simultaneously in one tissue section by using a high-resolution laser with a mass cytometer. However, due to its high resolution and large number of channels, how to process and interpret the image data from IMC remains a key challenge to its further applications. Accurate and reliable single cell segmentation is the first and a critical step to process IMC image data. Unfortunately, existing segmentation pipelines either produce inaccurate cell segmentation results or require manual annotation, which is very time consuming. Here, we developed Dice-XMBD, a Deep learnIng-based Cell sEgmentation algorithm for tissue multiplexed imaging data. In comparison with other state-of-the-art cell segmentation methods currently used for IMC images, Dice-XMBD generates more accurate single cell masks efficiently on IMC images produced with different nuclear, membrane, and cytoplasm markers. All codes and datasets are available at https://github.com/xmuyulab/Dice-XMBD. Frontiers Media S.A. 2021-09-15 /pmc/articles/PMC8480472/ /pubmed/34603385 http://dx.doi.org/10.3389/fgene.2021.721229 Text en Copyright © 2021 Xiao, Qiao, Jiao, Fu, Yang, Wang, Yu and Han. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Xiao, Xu
Qiao, Ying
Jiao, Yudi
Fu, Na
Yang, Wenxian
Wang, Liansheng
Yu, Rongshan
Han, Jiahuai
Dice-XMBD: Deep Learning-Based Cell Segmentation for Imaging Mass Cytometry
title Dice-XMBD: Deep Learning-Based Cell Segmentation for Imaging Mass Cytometry
title_full Dice-XMBD: Deep Learning-Based Cell Segmentation for Imaging Mass Cytometry
title_fullStr Dice-XMBD: Deep Learning-Based Cell Segmentation for Imaging Mass Cytometry
title_full_unstemmed Dice-XMBD: Deep Learning-Based Cell Segmentation for Imaging Mass Cytometry
title_short Dice-XMBD: Deep Learning-Based Cell Segmentation for Imaging Mass Cytometry
title_sort dice-xmbd: deep learning-based cell segmentation for imaging mass cytometry
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8480472/
https://www.ncbi.nlm.nih.gov/pubmed/34603385
http://dx.doi.org/10.3389/fgene.2021.721229
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