Automated 3D Labelling of Fibroblasts and Endothelial Cells in SEM-Imaged Placenta using Deep Learning

Benita S. Mackay; Sophie Blundell; Olivia Etter; Yunhui Xie; Michael D. T. McDonnel; Matthew Praeger; James Grant-Jacob; Robert Eason; Rohan Lewis; Ben Mills

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Paper

Automated 3D Labelling of Fibroblasts and Endothelial Cells in SEM-Imaged Placenta using Deep Learning

Topics: Applications of Machine Learning; Deep Learning in Bioimaging; Histology and Tissue Imaging; Image Processing Methods; Medical Imaging and Diagnosis

In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOSTEC, 46-53, 2020 , Valletta, Malta

Authors: Benita S. Mackay ¹ ; Sophie Blundell ² ; Olivia Etter ³ ; Yunhui Xie ¹ ; Michael D. T. McDonnel ¹ ; Matthew Praeger ¹ ; James Grant-Jacob ¹ ; Robert Eason ¹ ; Rohan Lewis ³ and Ben Mills ¹

Affiliations: ¹ Optoelectronics Research Centre, University of Southampton, Southampton, U.K. ; ² Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, U.K. ; ³ Faculty of Medicine, University of Southampton, Southampton, U.K.

Keyword(s): 3D Image Processing, Deep Learning, SBFSEM Images, Placenta.

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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Paper citation in several formats:

Mackay, B.; Blundell, S.; Etter, O.; Xie, Y.; McDonnel, M.; Praeger, M.; Grant-Jacob, J.; Eason, R.; Lewis, R. and Mills, B. (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 (BIOSTEC 2020) - BIOIMAGING; ISBN 978-989-758-398-8; ISSN 2184-4305, SciTePress, pages 46-53. DOI: 10.5220/0008949700460053

@conference{bioimaging20,
author={Benita S. Mackay. and Sophie Blundell. and Olivia Etter. and Yunhui Xie. and Michael D. T. McDonnel. and Matthew Praeger. and James Grant{-}Jacob. and Robert Eason. and Rohan Lewis. and Ben Mills.},
title={Automated 3D Labelling of Fibroblasts and Endothelial Cells in SEM-Imaged Placenta using Deep Learning},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - BIOIMAGING},
year={2020},
pages={46-53},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008949700460053},
isbn={978-989-758-398-8},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - BIOIMAGING
TI - Automated 3D Labelling of Fibroblasts and Endothelial Cells in SEM-Imaged Placenta using Deep Learning
SN - 978-989-758-398-8
IS - 2184-4305
AU - Mackay, B.
AU - Blundell, S.
AU - Etter, O.
AU - Xie, Y.
AU - McDonnel, M.
AU - Praeger, M.
AU - Grant-Jacob, J.
AU - Eason, R.
AU - Lewis, R.
AU - Mills, B.
PY - 2020
SP - 46
EP - 53
DO - 10.5220/0008949700460053
PB - SciTePress