Automated 3D labelling of fibroblasts in SEM-imaged placenta using deep learning
Automated 3D labelling of fibroblasts 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.
Deep learning can be used to automate the labelling process for faster 3D analysis. Here, a deep neural network was trained to label a fibroblast from serial block face scanning electron microscopy (SBFSEM) placental imaging.
poster, Placenta, Deep Learning, 3D imaging
MacKay, Benita, Scout
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Blundell, Sophie
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Lewis, Rohan
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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, Robert
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Mills, Benjamin
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22 October 2019
MacKay, Benita, Scout
318d298f-5b38-43d7-b30d-8cd07f69acd4
Blundell, Sophie
ae6f5834-292d-40fc-8b13-82dc35c66c28
Lewis, Rohan
caaeb97d-ea69-4f7b-8adb-5fa25e2d3502
Etter, Olivia
65205ba0-c67e-4125-8455-e325f698d099
Xie, Yunhui
f2c3b0e4-8650-4e04-80e5-04505f45bdd6
McDonnell, Michael, David Tom
bc7b6423-bd77-424d-81e7-4e5448e926cb
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Eason, Robert
e38684c3-d18c-41b9-a4aa-def67283b020
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
MacKay, Benita, Scout, Blundell, Sophie, Lewis, Rohan, Etter, Olivia, Xie, Yunhui, McDonnell, Michael, David Tom, Praeger, Matthew, Grant-Jacob, James, Eason, Robert and Mills, Benjamin
(2019)
Automated 3D labelling of fibroblasts in SEM-imaged placenta using deep learning.
The Southampton Machine Intelligence Showcase 2019, University of Southampton, Southampton, United Kingdom.
22 Oct 2019.
1 pp
.
Record type:
Conference or Workshop Item
(Poster)
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.
Deep learning can be used to automate the labelling process for faster 3D analysis. Here, a deep neural network was trained to label a fibroblast from serial block face scanning electron microscopy (SBFSEM) placental imaging.
Text
Automated 3D labelling of fibroblasts in placenta
- Author's Original
More information
Submitted date: 7 October 2019
Published date: 22 October 2019
Venue - Dates:
The Southampton Machine Intelligence Showcase 2019, University of Southampton, Southampton, United Kingdom, 2019-10-22 - 2019-10-22
Keywords:
poster, Placenta, Deep Learning, 3D imaging
Identifiers
Local EPrints ID: 434872
URI: http://eprints.soton.ac.uk/id/eprint/434872
PURE UUID: 73856517-1044-4835-baca-d713b3a70ab4
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Date deposited: 14 Oct 2019 16:30
Last modified: 17 Mar 2024 03:22
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