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Cell membrane feature detection using graph neural networks
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Offord, Edward, Lutton, E. Josiah and Bretschneider, Till (2023) Cell membrane feature detection using graph neural networks. In: 20th IEEE International Symposium on Biomedical Imaging, Cartagena de Indias, Colombia, 18-21 Apr 2023. Published in: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) ISBN 9781665473583. doi:10.1109/ISBI53787.2023.10230695 ISSN 1945-8452.
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WRAP-cell-membrane-feature-detection-using-graph-neural-networks-2023.pdf - Accepted Version - Requires a PDF viewer. Download (1215Kb) | Preview |
Official URL: https://doi.org/10.1109/ISBI53787.2023.10230695
Abstract
Many cellular processes involve complex deformations of the cell surface, which are difficult to automatically detect and analyse in 3D microscopy images. One issue faced by modern machine learning methods is that 3D microscopy images are large and require a high computational load to analyse. To simplify this problem, we propose a graph convolutional neural network applied to a triangulated mesh of the cell surface, where nodes are associated with geometric and intensity features of biomarkers on or near the surface. Here, we focus on identification of macropinocytic cups on the surface of Dictyostelium cells, structures involved in the uptake of extracellular fluid. The network classifies each node into belonging to a cup or not, enabling subsequent studies of the detailed distribution of molecules regulating fluid uptake in cells. We show that a simple network architecture can identify key features of the surface, suggesting that these methods have strong potential for advancing the analysis of cell surface dynamics.
Item Type: | Conference Item (Paper) | |||||||||
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Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QH Natural history |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||
Library of Congress Subject Headings (LCSH): | Cytology -- Computer simulation, Neural networks (Computer science), Graph theory, Cell membranes -- Computer simulation | |||||||||
Journal or Publication Title: | 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) | |||||||||
Publisher: | IEEE | |||||||||
ISBN: | 9781665473583 | |||||||||
ISSN: | 1945-8452 | |||||||||
Official Date: | 1 September 2023 | |||||||||
Dates: |
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DOI: | 10.1109/ISBI53787.2023.10230695 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Reuse Statement (publisher, data, author rights): | © 2023 Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||
Date of first compliant deposit: | 8 March 2023 | |||||||||
Date of first compliant Open Access: | 9 March 2023 | |||||||||
RIOXX Funder/Project Grant: |
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Conference Paper Type: | Paper | |||||||||
Title of Event: | 20th IEEE International Symposium on Biomedical Imaging | |||||||||
Type of Event: | Conference | |||||||||
Location of Event: | Cartagena de Indias, Colombia | |||||||||
Date(s) of Event: | 18-21 Apr 2023 | |||||||||
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