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...

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Veröffentlicht in:	Scientific data Jg. 10; H. 1; S. 75 - 17
Hauptverfasser:	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.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 06.02.2023 Nature Publishing Group Nature Portfolio
Schlagworte:	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
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Abstract	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.
AbstractList	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. 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. 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.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. Abstract 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.
ArticleNumber	75
Author	Salem, Osama E. Rittscher, Jens Ghatwary, Noha Riegler, Michael A. Halvorsen, Pål Cannizzaro, Renato Jha, Debesh Realdon, Stefano Petlund, Andreas Ali, Sharib Daul, Christian East, James E. Anonsen, Kim V. Lamarque, Dominique de Lange, Thomas
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Snippet	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... 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... Abstract Polyps in the colon are widely known cancer precursors identified by colonoscopy. Whilst most polyps are benign, the polyp’s number, size and surface...
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SubjectTerms	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
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Title	A multi-centre polyp detection and segmentation dataset for generalisability assessment
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