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dc.contributor.authorAli, Sharib
dc.contributor.authorJha, Debesh
dc.contributor.authorGhatwary, Noha
dc.contributor.authorRealdon, Stefano
dc.contributor.authorCannizzaro, Renato
dc.contributor.authorSalem, Osama E.
dc.contributor.authorLamarque, Dominique
dc.contributor.authorDaul, Christian
dc.contributor.authorRiegler, Michael Alexander
dc.contributor.authorÅnonsen, Kim Vidar
dc.contributor.authorPetlund, Andreas
dc.contributor.authorHalvorsen, Pål
dc.contributor.authorRittscher, Jens
dc.contributor.authorde Lange, Thomas
dc.contributor.authorEast, James E
dc.date.accessioned2023-03-03T11:31:13Z
dc.date.available2023-03-03T11:31:13Z
dc.date.issued2023-02-06
dc.description.abstractPolyps in the colon are widely known cancer precursors identifed by colonoscopy. Whilst most polyps are benign, the polyp’s number, size and surface structure are linked to the risk of colon cancer. Several methods have been developed to automate polyp detection and segmentation. However, the main issue is that they are not tested rigorously on a large multicentre purpose-built dataset, one reason being the lack of a comprehensive public dataset. As a result, the developed methods may not generalise to diferent population datasets. To this extent, we have curated a dataset from six unique centres incorporating more than 300 patients. The dataset includes both single frame and sequence data with 3762 annotated polyp labels with precise delineation of polyp boundaries verifed by six senior gastroenterologists. To our knowledge, this is the most comprehensive detection and pixellevel segmentation dataset (referred to as PolypGen) curated by a team of computational scientists and expert gastroenterologists. The paper provides insight into data construction and annotation strategies, quality assurance, and technical validation.en_US
dc.identifier.citationAli, Jha, Ghatwary, Realdon, Cannizzaro, Salem, Lamarque, Daul, Riegler, Ånonsen, Petlund, Halvorsen, Rittscher, de Lange, East. A multi-centre polyp detection and segmentation dataset for generalisability assessment. Scientific Data. 2023;10en_US
dc.identifier.cristinIDFRIDAID 2124105
dc.identifier.doi10.1038/s41597-023-01981-y
dc.identifier.issn2052-4463
dc.identifier.urihttps://hdl.handle.net/10037/28669
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
dc.relation.journalScientific Data
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleA multi-centre polyp detection and segmentation dataset for generalisability assessmenten_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Med mindre det står noe annet, er denne innførselens lisens beskrevet som Attribution 4.0 International (CC BY 4.0)