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Learning to segment microscopy images with lazy labels

hal.structure.identifierDepartment of Applied Mathematics and Theoretical Physics [DAMTP]
dc.contributor.authorKE, Rihuan
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorBUGEAU, Aurélie
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorPAPADAKIS, Nicolas
hal.structure.identifierUnilever R&D
dc.contributor.authorSCHUETZ, Peter
hal.structure.identifierDepartment of Applied Mathematics and Theoretical Physics [DAMTP]
dc.contributor.authorSCHÖNLIEB, Carola-Bibiane
dc.date.accessioned2024-04-04T02:49:52Z
dc.date.available2024-04-04T02:49:52Z
dc.date.issued2020-08-23
dc.date.conference2020-08-23
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/191857
dc.description.abstractEnThe need for labour intensive pixel-wise annotation is a major limitation of many fully supervised learning methods for segmenting bioimages that can contain numerous object instances with thin separations. In this paper, we introduce a deep convolutional neural network for microscopy image segmentation. Annotation issues are circumvented by letting the network being trainable on coarse labels combined with only a very small number of images with pixel-wise annotations. We call this new labelling strategy `lazy' labels. Image segmentation is stratified into three connected tasks: rough inner region detection, object separation and pixel-wise segmentation. These tasks are learned in an end-to-end multi-task learning framework. The method is demonstrated on two microscopy datasets, where we show that the model gives accurate segmentation results even if exact boundary labels are missing for a majority of annotated data. It brings more flexibility and efficiency for training deep neural networks that are data hungry and is applicable to biomedical images with poor contrast at the object boundaries or with diverse textures and repeated patterns.
dc.language.isoen
dc.subject.enMulti-task learning
dc.subject.enConvolutional neural networks
dc.subject.enImage segmentation
dc.title.enLearning to segment microscopy images with lazy labels
dc.typeCommunication dans un congrès
dc.subject.halInformatique [cs]/Traitement du signal et de l'image
dc.subject.halInformatique [cs]/Apprentissage [cs.LG]
dc.subject.halInformatique [cs]/Intelligence artificielle [cs.AI]
dc.identifier.arxiv1906.12177v2
dc.description.sponsorshipEuropeNonlocal Methods for Arbitrary Data Sources
bordeaux.page411-428
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.conference.titleECCV Workshop on BioImage Computing (BIC'20)
bordeaux.countryGB
bordeaux.conference.cityGlasgow
bordeaux.peerReviewedoui
hal.identifierhal-02170180
hal.version1
hal.invitednon
hal.proceedingsoui
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02170180v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2020-08-23&rft.spage=411-428&rft.epage=411-428&rft.au=KE,%20Rihuan&BUGEAU,%20Aur%C3%A9lie&PAPADAKIS,%20Nicolas&SCHUETZ,%20Peter&SCH%C3%96NLIEB,%20Carola-Bibiane&rft.genre=unknown


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