A multi-centre polyp detection and segmentation dataset for generalisability assessment.
Fiche publication
Date publication
février 2023
Journal
Scientific data
Auteurs
Membres identifiés du Cancéropôle Est :
Pr DAUL Christian
Tous les auteurs :
Ali S, Jha D, Ghatwary N, Realdon S, Cannizzaro R, Salem OE, Lamarque D, Daul C, Riegler MA, Anonsen KV, Petlund A, Halvorsen P, Rittscher J, de Lange T, East JE
Lien Pubmed
Résumé
Polyps in the colon are widely known cancer precursors identified 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 different 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 verified by six senior gastroenterologists. To our knowledge, this is the most comprehensive detection and pixel-level 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.
Mots clés
Humans, Colonic Polyps, diagnosis, Colonoscopy, methods, Colonic Neoplasms
Référence
Sci Data. 2023 02 6;10(1):75