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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)
Verlagsversion Forschungsdaten 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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Cell Biology ; Neural Networks ; Predictive Medicine
ISSN (print) / ISBN 2589-0042
e-ISSN 2589-0042
Zeitschrift iScience
Quellenangaben Band: 25, Heft: 11, Seiten: , Artikelnummer: 105298 Supplement: ,
Verlag Elsevier
Verlagsort Amsterdam ; Bosten ; London ; New York ; Oxford ; Paris ; Philadelphia ; San Diego ; St. Louis
Begutachtungsstatus Peer reviewed
Institut(e) Human-Centered AI (HCA)
Helmholtz Pioneer Campus (HPC)
Institute of Computational Biology (ICB)
Förderungen Horizon 2020
European Research Council
Horizon 2020 Framework Programme
Mohammad Mirkazemi