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dc.contributor.authorSekh, Arif Ahmed
dc.contributor.authorOpstad, Ida Sundvor
dc.contributor.authorBirgisdottir, Åsa B.
dc.contributor.authorMyrmel, Truls
dc.contributor.authorAhluwalia, Balpreet Singh
dc.contributor.authorAgarwal, Krishna
dc.contributor.authorPrasad, Dilip K.
dc.date.accessioned2021-01-18T08:13:31Z
dc.date.available2021-01-18T08:13:31Z
dc.date.issued2020-08-05
dc.description.abstractDetecting and analyzing nanoscale motion patterns of vesicles, smaller than the microscope resolution (~250 nm), inside living biological cells is a challenging problem. State-of-the-art CV approaches based on detection, tracking, optical flow or deep learning perform poorly for this problem. We propose an integrative approach, built upon physics based simulations, nanoscopy algorithms, and shallow residual attention network to make it possible for the first time to analysis sub-resolution motion patterns in vesicles that may also be of sub-resolution diameter. Our results show state-of-the-art performance, 89% validation accuracy on simulated dataset and 82% testing accuracy on an experimental dataset of living heart muscle cells imaged under three different pathological conditions. We demonstrate automated analysis of the motion states and changed in them for over 9000 vesicles. Such analysis will enable large scale biological studies of vesicle transport and interaction in living cells in the future.en_US
dc.identifier.citationSekh, A.A., Opstad, I.S., Birgisdottir, Å.S., Myrmel, T., Ahluwalia, B.S., Agarwal, K. & Prasad, D. (2020). Learning nanoscale motion patterns of vesicles in living cells. <i>2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020</i>, pp. 14011-14020.en_US
dc.identifier.cristinIDFRIDAID 1849837
dc.identifier.doi10.1109/CVPR42600.2020.01403
dc.identifier.urihttps://hdl.handle.net/10037/20305
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.ispartofOpstad, I.S. (2021). Bringing optical nanoscopy to life - Super-resolution microscopy of living cells. (Doctoral thesis). <a href=https://hdl.handle.net/10037/20306>https://hdl.handle.net/10037/20306</a>
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/NANO2021/288565/Norway/Integrated photonic chip-based nanoscopy for pathology & the clinic//en_US
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/804233/EU/Label-free 3D morphological nanoscopy for studying sub-cellular dynamics in live cancer cells with high spatio-temporal resolution/3D-nanoMorph/en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2020 The Author(s)en_US
dc.subjectVDP::Technology: 500::Medical technology: 620en_US
dc.subjectVDP::Teknologi: 500::Medisinsk teknologi: 620en_US
dc.titleLearning Nanoscale Motion Patterns of Vesicles in Living Cellsen_US
dc.type.versionacceptedVersionen_US
dc.typeConference objecten_US
dc.typeKonferansebidragen_US


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