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MR to CT synthesis using GANs : a practical guide applied to thoracic imaging

hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorLONGUEFOSSE, Arthur
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
dc.contributor.authorDENIS DE SENNEVILLE, Baudouin
hal.structure.identifierCentre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
dc.contributor.authorDOURNES, Gaël
hal.structure.identifierCentre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
dc.contributor.authorBENLALA, Ilyes
hal.structure.identifierCentre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
dc.contributor.authorLAURENT, François
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorDESBARATS, Pascal
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorBALDACCI, Fabien
dc.date.accessioned2024-04-04T02:32:50Z
dc.date.available2024-04-04T02:32:50Z
dc.date.issued2023-03
dc.date.conference2023-02-19
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/190420
dc.description.abstractEnIn medical imaging, MR-to-CT synthesis has been extensively studied. The primary motivation is to benefit from the quality of the CT signal, i.e. excellent spatial resolution, high contrast, and sharpness, while avoiding patient exposure to CT ionizing radiation, by relying on the safe and non-invasive nature of MRI. Recent studies have successfully used deep learning methods for cross-modality synthesis, notably with the use of conditional Generative Adversarial Networks (cGAN), due to their ability to create realistic images in a target domain from an input in a source domain. In this study, we examine in detail the different steps required for cross-modality translation using GANs applied to MR-to-CT lung synthesis, from data representation and pre-processing to the type of method and loss function selection. The different alternatives for each step were evaluated using a quantitative comparison of intensities inside the lungs, as well as bronchial segmentations between synthetic and ground truth CTs. Finally, a general guideline for crossmodality medical synthesis is proposed, bringing together best practices from generation to evaluation.
dc.language.isoen
dc.publisherSCITEPRESS - Science and Technology Publications
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.subject.enGAN
dc.subject.enCT Synthesis
dc.subject.enLung
dc.title.enMR to CT synthesis using GANs : a practical guide applied to thoracic imaging
dc.typeCommunication dans un congrès
dc.identifier.doi10.5220/0011895700003417
dc.subject.halSciences de l'Homme et Société
dc.subject.halInformatique [cs]
dc.subject.halSciences du Vivant [q-bio]
bordeaux.page268-274
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.conference.titleVISIGRAPP 2023 - International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
bordeaux.countryPT
bordeaux.conference.cityLisbon
bordeaux.peerReviewedoui
hal.identifierhal-04268701
hal.version1
hal.invitednon
hal.proceedingsoui
hal.conference.end2023-02-19
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-04268701v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2023-03&rft.spage=268-274&rft.epage=268-274&rft.au=LONGUEFOSSE,%20Arthur&DENIS%20DE%20SENNEVILLE,%20Baudouin&DOURNES,%20Ga%C3%ABl&BENLALA,%20Ilyes&LAURENT,%20Fran%C3%A7ois&rft.genre=unknown


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