A multi-centre polyp detection and segmentation dataset for generalisability assessment

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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Scientific data Ročník 10; číslo 1; s. 75 - 17
Hlavní autori: Ali, Sharib, Jha, Debesh, Ghatwary, Noha, Realdon, Stefano, Cannizzaro, Renato, Salem, Osama E., Lamarque, Dominique, Daul, Christian, Riegler, Michael A., Anonsen, Kim V., Petlund, Andreas, Halvorsen, Pål, Rittscher, Jens, de Lange, Thomas, East, James E.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 06.02.2023
Nature Publishing Group
Nature Portfolio
Predmet:
639/705/117
692/699/1503/1504/1885/1393
Colon cancer
Colonic Neoplasms
Colonic Polyps
Colonic Polyps - diagnosis
Colonoscopy
Colonoscopy - methods
Colorectal cancer
Computer applications
Computer graphics and computer vision
Computer Science
Computer Sciences
Data Descriptor
Datasets
Datavetenskap (datalogi)
Datorgrafik och datorseende
diagnosis
Gastroenterologi och hepatologi
Gastroenterology
Gastroenterology and Hepatology
Human health and pathology
Humanities and Social Sciences
Humans
Hépatology and Gastroenterology
Image Processing
Life Sciences
Medical Imaging
methods
multidisciplinary
Polyps
Quality assurance
Science
Science (multidisciplinary)
Segmentation
ISSN:2052-4463, 2052-4463
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Article-2
ObjectType-Undefined-1
ObjectType-Feature-3
content type line 23
ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-023-01981-y