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dc.contributor.author
Gomariz Carrillo, Álvaro
dc.contributor.author
Portenier, Tiziano
dc.contributor.author
Nombela-Arrieta, César
dc.contributor.author
Goksel, Orcun
dc.date.accessioned
2022-08-08T13:04:50Z
dc.date.available
2022-02-13T05:51:32Z
dc.date.available
2022-05-10T08:05:02Z
dc.date.available
2022-08-08T13:04:50Z
dc.date.issued
2022-02-04
dc.identifier.issn
2375-2548
dc.identifier.other
10.1126/sciadv.abi8295
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/532275
dc.identifier.doi
10.3929/ethz-b-000532275
dc.description.abstract
The investigation of biological systems with three-dimensional microscopy demands automatic cell identification methods that not only are accurate but also can imply the uncertainty in their predictions. The use of deep learning to regress density maps is a popular successful approach for extracting cell coordinates from local peaks in a postprocessing step, which then, however, hinders any meaningful probabilistic output. We propose a framework that can operate on large microscopy images and output probabilistic predictions (i) by integrating deep Bayesian learning for the regression of uncertainty-aware density maps, where peak detection algorithms generate cell proposals, and (ii) by learning a mapping from prediction proposals to a probabilistic space that accurately represents the chances of a successful prediction. Using these calibrated predictions, we propose a probabilistic spatial analysis with Monte Carlo sampling. We demonstrate this in a bone marrow dataset, where our proposed methods reveal spatial patterns that are otherwise undetectable.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
AAAS
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
Probabilistic spatial analysis in quantitative microscopy with uncertainty-aware cell detection using deep Bayesian regression
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
Science Advances
ethz.journal.volume
8
en_US
ethz.journal.issue
5
en_US
ethz.journal.abbreviated
Sci Adv
ethz.pages.start
eabi8295
en_US
ethz.size
16 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.grant
Imaging Soft Tissue Elasticity
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Washington, DC
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02652 - Institut für Bildverarbeitung / Computer Vision Laboratory::09528 - Göksel, Orçun (ehemalig) / Göksel, Orçun (former)
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02652 - Institut für Bildverarbeitung / Computer Vision Laboratory::09528 - Göksel, Orçun (ehemalig) / Göksel, Orçun (former)
ethz.grant.agreementno
179116
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
SNF-Förderungsprofessuren: Fortsetzungsgesuche
ethz.date.deposited
2022-02-13T05:51:44Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2022-05-10T08:05:09Z
ethz.rosetta.lastUpdated
2023-02-07T05:09:32Z
ethz.rosetta.versionExported
true
ethz.COinS
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