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Waibel, D.J.E. ; Kiermeyer, N. ; Atwell, S. ; Sadafi, A. ; Meier, M. ; Marr, C.

SHAPR predicts 3D cell shapes from 2D microscopic images.

iScience 25:105298 (2022)
Publ. Version/Full Text Research data DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Reconstruction of shapes and sizes of three-dimensional (3D) objects from two- dimensional (2D) information is an intensely studied subject in computer vision. We here consider the level of single cells and nuclei and present a neural network-based SHApe PRediction autoencoder. For proof-of-concept, SHAPR reconstructs 3D shapes of red blood cells from single view 2D confocal microscopy images more accurately than naïve stereological models and significantly increases the feature-based prediction of red blood cell types from F1 = 79% to F1 = 87.4%. Applied to 2D images containing spheroidal aggregates of densely grown human induced pluripotent stem cells, we find that SHAPR learns fundamental shape properties of cell nuclei and allows for prediction-based morphometry. Reducing imaging time and data storage, SHAPR will help to optimize and up-scale image-based high-throughput applications for biomedicine.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Cell Biology ; Neural Networks ; Predictive Medicine
Language english
Publication Year 2022
HGF-reported in Year 2022
ISSN (print) / ISBN 2589-0042
e-ISSN 2589-0042
Journal iScience
Quellenangaben Volume: 25, Issue: 11, Pages: , Article Number: 105298 Supplement: ,
Publisher Elsevier
Publishing Place Amsterdam ; Bosten ; London ; New York ; Oxford ; Paris ; Philadelphia ; San Diego ; St. Louis
Reviewing status Peer reviewed
Institute(s) Institute of AI for Health (AIH)
Helmholtz Pioneer Campus (HPC)
Institute of Computational Biology (ICB)
POF-Topic(s) 30205 - Bioengineering and Digital Health
30201 - Metabolic Health
Research field(s) Enabling and Novel Technologies
Pioneer Campus
PSP Element(s) G-540007-001
G-510002-001
G-503800-001
Grants Horizon 2020
European Research Council
Horizon 2020 Framework Programme
Mohammad Mirkazemi
DOI 10.1016/j.isci.2022.105298
WOS ID WOS:000886608300006
Scopus ID 85140045924
PubMed ID 36304119
Erfassungsdatum 2022-10-28
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