Detection and classification of neurons and glial cells in the MADM mouse brain using RetinaNet.

The ability to automatically detect and classify populations of cells in tissue sections is paramount in a wide variety of applications ranging from developmental biology to pathology. Although deep learning algorithms are widely applied to microscopy data, they typically focus on segmentation which...

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Autores principales:	Yuheng Cai, Xuying Zhang, Shahar Z Kovalsky, H Troy Ghashghaei, Alon Greenbaum
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8d8d4ca159c041cfaf050e1cf75338e6
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spelling	oai:doaj.org-article:8d8d4ca159c041cfaf050e1cf75338e62021-12-02T20:08:00ZDetection and classification of neurons and glial cells in the MADM mouse brain using RetinaNet.1932-620310.1371/journal.pone.0257426https://doaj.org/article/8d8d4ca159c041cfaf050e1cf75338e62021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0257426https://doaj.org/toc/1932-6203The ability to automatically detect and classify populations of cells in tissue sections is paramount in a wide variety of applications ranging from developmental biology to pathology. Although deep learning algorithms are widely applied to microscopy data, they typically focus on segmentation which requires extensive training and labor-intensive annotation. Here, we utilized object detection networks (neural networks) to detect and classify targets in complex microscopy images, while simplifying data annotation. To this end, we used a RetinaNet model to classify genetically labeled neurons and glia in the brains of Mosaic Analysis with Double Markers (MADM) mice. Our initial RetinaNet-based model achieved an average precision of 0.90 across six classes of cells differentiated by MADM reporter expression and their phenotype (neuron or glia). However, we found that a single RetinaNet model often failed when encountering dense and saturated glial clusters, which show high variability in their shape and fluorophore densities compared to neurons. To overcome this, we introduced a second RetinaNet model dedicated to the detection of glia clusters. Merging the predictions of the two computational models significantly improved the automated cell counting of glial clusters. The proposed cell detection workflow will be instrumental in quantitative analysis of the spatial organization of cellular populations, which is applicable not only to preparations in neuroscience studies, but also to any tissue preparation containing labeled populations of cells.Yuheng CaiXuying ZhangShahar Z KovalskyH Troy GhashghaeiAlon GreenbaumPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0257426 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yuheng Cai Xuying Zhang Shahar Z Kovalsky H Troy Ghashghaei Alon Greenbaum Detection and classification of neurons and glial cells in the MADM mouse brain using RetinaNet.
description	The ability to automatically detect and classify populations of cells in tissue sections is paramount in a wide variety of applications ranging from developmental biology to pathology. Although deep learning algorithms are widely applied to microscopy data, they typically focus on segmentation which requires extensive training and labor-intensive annotation. Here, we utilized object detection networks (neural networks) to detect and classify targets in complex microscopy images, while simplifying data annotation. To this end, we used a RetinaNet model to classify genetically labeled neurons and glia in the brains of Mosaic Analysis with Double Markers (MADM) mice. Our initial RetinaNet-based model achieved an average precision of 0.90 across six classes of cells differentiated by MADM reporter expression and their phenotype (neuron or glia). However, we found that a single RetinaNet model often failed when encountering dense and saturated glial clusters, which show high variability in their shape and fluorophore densities compared to neurons. To overcome this, we introduced a second RetinaNet model dedicated to the detection of glia clusters. Merging the predictions of the two computational models significantly improved the automated cell counting of glial clusters. The proposed cell detection workflow will be instrumental in quantitative analysis of the spatial organization of cellular populations, which is applicable not only to preparations in neuroscience studies, but also to any tissue preparation containing labeled populations of cells.
format	article
author	Yuheng Cai Xuying Zhang Shahar Z Kovalsky H Troy Ghashghaei Alon Greenbaum
author_facet	Yuheng Cai Xuying Zhang Shahar Z Kovalsky H Troy Ghashghaei Alon Greenbaum
author_sort	Yuheng Cai
title	Detection and classification of neurons and glial cells in the MADM mouse brain using RetinaNet.
title_short	Detection and classification of neurons and glial cells in the MADM mouse brain using RetinaNet.
title_full	Detection and classification of neurons and glial cells in the MADM mouse brain using RetinaNet.
title_fullStr	Detection and classification of neurons and glial cells in the MADM mouse brain using RetinaNet.
title_full_unstemmed	Detection and classification of neurons and glial cells in the MADM mouse brain using RetinaNet.
title_sort	detection and classification of neurons and glial cells in the madm mouse brain using retinanet.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/8d8d4ca159c041cfaf050e1cf75338e6
work_keys_str_mv	AT yuhengcai detectionandclassificationofneuronsandglialcellsinthemadmmousebrainusingretinanet AT xuyingzhang detectionandclassificationofneuronsandglialcellsinthemadmmousebrainusingretinanet AT shaharzkovalsky detectionandclassificationofneuronsandglialcellsinthemadmmousebrainusingretinanet AT htroyghashghaei detectionandclassificationofneuronsandglialcellsinthemadmmousebrainusingretinanet AT alongreenbaum detectionandclassificationofneuronsandglialcellsinthemadmmousebrainusingretinanet
_version_	1718375254746726400

Detection and classification of neurons and glial cells in the MADM mouse brain using RetinaNet.

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