PuSH - Publication Server of Helmholtz Zentrum München

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.
Altmetric
Additional Metrics?
[➜Log in]
Edit extra informations Login
Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Cell Biology ; Neural Networks ; Predictive Medicine
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
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of AI for Health (AIH)
Helmholtz Pioneer Campus (HPC)
Institute of Computational Biology (ICB)
Grants Horizon 2020
European Research Council
Horizon 2020 Framework Programme
Mohammad Mirkazemi