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Automated 3D labelling of fibroblasts and endothelial cells in SEM-imaged placenta using deep learning

Automated 3D labelling of fibroblasts and endothelial cells in SEM-imaged placenta using deep learning
Automated 3D labelling of fibroblasts and endothelial cells in SEM-imaged placenta using deep learning
Analysis of fibroblasts within placenta is necessary for research into placental growth-factors, which are linked to lifelong health and chronic disease risk. 2D analysis of fibroblasts can be challenging due to the variation and complexity of their structure. 3D imaging can provide important visualisation, but the images produced are extremely labour intensive to construct because of the extensive manual processing required. Machine learning can be used to automate the labelling process for faster 3D analysis. Here, a deep neural network is trained to label a fibroblast from serial block face scanning electron microscopy (SBFSEM) placental imaging.
46-53
Science and Technology Publications, Lda
MacKay, Benita
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Blundell, Sophie
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Etter, Olivia
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Xie, Yunhui
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McDonnell, Michael, David Tom
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Praeger, Matthew
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Grant-Jacob, James
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Eason, R.W.
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Lewis, Rohan
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Mills, Benjamin
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MacKay, Benita
318d298f-5b38-43d7-b30d-8cd07f69acd4
Blundell, Sophie
ae6f5834-292d-40fc-8b13-82dc35c66c28
Etter, Olivia
65205ba0-c67e-4125-8455-e325f698d099
Xie, Yunhui
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McDonnell, Michael, David Tom
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Praeger, Matthew
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Grant-Jacob, James
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Eason, R.W.
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Lewis, Rohan
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Mills, Benjamin
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MacKay, Benita, Blundell, Sophie, Etter, Olivia, Xie, Yunhui, McDonnell, Michael, David Tom, Praeger, Matthew, Grant-Jacob, James, Eason, R.W., Lewis, Rohan and Mills, Benjamin (2020) Automated 3D labelling of fibroblasts and endothelial cells in SEM-imaged placenta using deep learning. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2 BIOIMAGING. Science and Technology Publications, Lda. pp. 46-53 . (doi:10.5220/0008949700460053).

Record type: Conference or Workshop Item (Paper)

Abstract

Analysis of fibroblasts within placenta is necessary for research into placental growth-factors, which are linked to lifelong health and chronic disease risk. 2D analysis of fibroblasts can be challenging due to the variation and complexity of their structure. 3D imaging can provide important visualisation, but the images produced are extremely labour intensive to construct because of the extensive manual processing required. Machine learning can be used to automate the labelling process for faster 3D analysis. Here, a deep neural network is trained to label a fibroblast from serial block face scanning electron microscopy (SBFSEM) placental imaging.

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More information

Submitted date: 23 October 2019
Accepted/In Press date: 3 December 2019
Published date: 26 February 2020
Additional Information: ISBN 978-989-758-398-8
Venue - Dates: BIOSTEC: BIOIMAGING 2020. INSTICC, Malta, 2020-02-24 - 2020-02-26

Identifiers

Local EPrints ID: 436579
URI: http://eprints.soton.ac.uk/id/eprint/436579
PURE UUID: 350e8346-dae2-4e6f-ba23-e20982dd0f99
ORCID for Benita MacKay: ORCID iD orcid.org/0000-0003-2050-8912
ORCID for James Grant-Jacob: ORCID iD orcid.org/0000-0002-4270-4247
ORCID for R.W. Eason: ORCID iD orcid.org/0000-0001-9704-2204
ORCID for Rohan Lewis: ORCID iD orcid.org/0000-0003-4044-9104
ORCID for Benjamin Mills: ORCID iD orcid.org/0000-0002-1784-1012

Catalogue record

Date deposited: 16 Dec 2019 17:30
Last modified: 16 May 2020 00:59

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