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3D time-lapse imaging of a mouse embryo using intensity diffraction tomography embedded inside a deep learning framework

hal.structure.identifierCommissariat à l'énergie atomique et aux énergies alternatives [CEA]
dc.contributor.authorPIERRÉ, William
hal.structure.identifierCommissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information [CEA-LETI]
dc.contributor.authorHERVÉ, Lionel
hal.structure.identifierLaboratoire Photonique, Numérique et Nanosciences [LP2N]
dc.contributor.authorPAVIOLO, Chiara
hal.structure.identifierDépartement Microtechnologies pour la Biologie et la Santé [DTBS]
dc.contributor.authorMANDULA, Ondrej
hal.structure.identifierLaboratoire d'Innovation pour les Technologies des Energies Nouvelles et les nanomatériaux [LITEN]
dc.contributor.authorREMONDIERE, Vincent
hal.structure.identifierCommissariat à l'énergie atomique et aux énergies alternatives [CEA]
dc.contributor.authorMORALES, Sophie
hal.structure.identifierDonnées, Apprentissage et Optimisation [DAO]
dc.contributor.authorGRUDININ, Sergei
hal.structure.identifierInstitute for Advanced Biosciences / Institut pour l'Avancée des Biosciences (Grenoble) [IAB]
dc.contributor.authorRAY, Pierre
hal.structure.identifierInstitute for Advanced Biosciences / Institut pour l'Avancée des Biosciences (Grenoble) [IAB]
dc.contributor.authorDHELLEMMES, Magali
hal.structure.identifierInstitute for Advanced Biosciences / Institut pour l'Avancée des Biosciences (Grenoble) [IAB]
dc.contributor.authorARNOULT, Christophe
hal.structure.identifierCommissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information [CEA-LETI]
dc.contributor.authorALLIER, Cédric
dc.date.accessioned2023-05-12T10:30:13Z
dc.date.available2023-05-12T10:30:13Z
dc.date.issued2022
dc.identifier.issn1559-128X
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/181389
dc.description.abstractEnWe present a compact 3D diffractive microscope that can be inserted directly in a cell incubator for long-term observation of developing organisms. Our setup is particularly simple and robust, since it does not include any moving parts and is compatible with commercial cell culture containers. It has been designed to image large specimens ( > 100 × 100 × 100 µ m 3 ) with subcellular resolution. The sample’s optical properties [refractive index (RI) and absorption] are reconstructed in 3D from intensity-only images recorded with different illumination angles produced by an LED array. The reconstruction is performed using the beam propagation method embedded inside a deep-learning network where the layers encode the optical properties of the object. This deep neural network is trained for a given multiangle intensity acquisition. After training, the weights of the neural network deliver the 3D distribution of the optical properties of the sample. The effect of spherical aberrations due to the sample holder/air interfaces are taken into account in the forward model. Using this approach, we performed time-lapse 3D imaging of preimplantation mouse embryos over six days. Images of embryos from a single cell (low-scattering regime) to the blastocyst stage (highly scattering regime) were successfully reconstructed. Due to its subcellular resolution, our system can provide quantitative information on the embryos’ development and viability. Hence, this technology opens what we believe to be novel opportunities for 3D label-free live-cell imaging of whole embryos or organoids over long observation times.
dc.language.isoen
dc.publisherOptical Society of America
dc.title.en3D time-lapse imaging of a mouse embryo using intensity diffraction tomography embedded inside a deep learning framework
dc.typeArticle de revue
dc.identifier.doi10.1364/AO.453910
dc.subject.halPhysique [physics]/Physique [physics]/Optique [physics.optics]
dc.subject.halInformatique [cs]/Apprentissage [cs.LG]
bordeaux.journalApplied optics
bordeaux.page3337
bordeaux.volume61
bordeaux.hal.laboratoriesLaboratoire Photonique, Numérique et Nanosciences (LP2N) - UMR 5298*
bordeaux.issue12
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-03874593
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03874593v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Applied%20optics&rft.date=2022&rft.volume=61&rft.issue=12&rft.spage=3337&rft.epage=3337&rft.eissn=1559-128X&rft.issn=1559-128X&rft.au=PIERR%C3%89,%20William&HERV%C3%89,%20Lionel&PAVIOLO,%20Chiara&MANDULA,%20Ondrej&REMONDIERE,%20Vincent&rft.genre=article


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