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SHAPR predicts 3D cell shapes from 2D microscopic images.
iScience 25:105298 (2022)
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
ISSN (print) / ISBN
2589-0042
e-ISSN
2589-0042
Journal
iScience
Quellenangaben
Volume: 25,
Issue: 11,
Article Number: 105298
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)
Helmholtz Pioneer Campus (HPC)
Institute of Computational Biology (ICB)
Grants
Horizon 2020
European Research Council
Horizon 2020 Framework Programme
Mohammad Mirkazemi
European Research Council
Horizon 2020 Framework Programme
Mohammad Mirkazemi