ANMAF: an automated neuronal morphology analysis framework using convolutional neural networks

Abstract Measurement of neuronal size is challenging due to their complex histology. Current practice includes manual or pseudo-manual measurement of somatic areas, which is labor-intensive and prone to human biases and intra-/inter-observer variances. We developed a novel high-throughput neuronal m...

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Autores principales:	Ling Tong, Rachel Langton, Joseph Glykys, Stephen Baek
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0b9ac4da742b4e18b54c5254bb314861
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spelling	oai:doaj.org-article:0b9ac4da742b4e18b54c5254bb3148612021-12-02T14:28:00ZANMAF: an automated neuronal morphology analysis framework using convolutional neural networks10.1038/s41598-021-87471-w2045-2322https://doaj.org/article/0b9ac4da742b4e18b54c5254bb3148612021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87471-whttps://doaj.org/toc/2045-2322Abstract Measurement of neuronal size is challenging due to their complex histology. Current practice includes manual or pseudo-manual measurement of somatic areas, which is labor-intensive and prone to human biases and intra-/inter-observer variances. We developed a novel high-throughput neuronal morphology analysis framework (ANMAF), using convolutional neural networks (CNN) to automatically contour the somatic area of fluorescent neurons in acute brain slices. Our results demonstrate considerable agreements between human annotators and ANMAF on detection, segmentation, and the area of somatic regions in neurons expressing a genetically encoded fluorophore. However, in contrast to humans, who exhibited significant variability in repeated measurements, ANMAF produced consistent neuronal contours. ANMAF was generalizable across different imaging protocols and trainable even with a small number of humanly labeled neurons. Our framework can facilitate more rigorous and quantitative studies of neuronal morphology by enabling the segmentation of many fluorescent neurons in thick brain slices in a standardized manner.Ling TongRachel LangtonJoseph GlykysStephen BaekNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ling Tong Rachel Langton Joseph Glykys Stephen Baek ANMAF: an automated neuronal morphology analysis framework using convolutional neural networks
description	Abstract Measurement of neuronal size is challenging due to their complex histology. Current practice includes manual or pseudo-manual measurement of somatic areas, which is labor-intensive and prone to human biases and intra-/inter-observer variances. We developed a novel high-throughput neuronal morphology analysis framework (ANMAF), using convolutional neural networks (CNN) to automatically contour the somatic area of fluorescent neurons in acute brain slices. Our results demonstrate considerable agreements between human annotators and ANMAF on detection, segmentation, and the area of somatic regions in neurons expressing a genetically encoded fluorophore. However, in contrast to humans, who exhibited significant variability in repeated measurements, ANMAF produced consistent neuronal contours. ANMAF was generalizable across different imaging protocols and trainable even with a small number of humanly labeled neurons. Our framework can facilitate more rigorous and quantitative studies of neuronal morphology by enabling the segmentation of many fluorescent neurons in thick brain slices in a standardized manner.
format	article
author	Ling Tong Rachel Langton Joseph Glykys Stephen Baek
author_facet	Ling Tong Rachel Langton Joseph Glykys Stephen Baek
author_sort	Ling Tong
title	ANMAF: an automated neuronal morphology analysis framework using convolutional neural networks
title_short	ANMAF: an automated neuronal morphology analysis framework using convolutional neural networks
title_full	ANMAF: an automated neuronal morphology analysis framework using convolutional neural networks
title_fullStr	ANMAF: an automated neuronal morphology analysis framework using convolutional neural networks
title_full_unstemmed	ANMAF: an automated neuronal morphology analysis framework using convolutional neural networks
title_sort	anmaf: an automated neuronal morphology analysis framework using convolutional neural networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0b9ac4da742b4e18b54c5254bb314861
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_version_	1718391233282310144

ANMAF: an automated neuronal morphology analysis framework using convolutional neural networks

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