دورية أكاديمية
A multi-centre polyp detection and segmentation dataset for generalisability assessment.
العنوان: | A multi-centre polyp detection and segmentation dataset for generalisability assessment. |
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المؤلفون: | Ali S; School of Computing, University of Leeds, LS2 9JT, Leeds, United Kingdom. s.s.ali@leeds.ac.uk.; Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, OX3 7DQ, Oxford, United Kingdom. s.s.ali@leeds.ac.uk.; Oxford National Institute for Health Research Biomedical Research centre, OX4 2PG, Oxford, United Kingdom. s.s.ali@leeds.ac.uk., Jha D; SimulaMet, Pilestredet 52, 0167, Oslo, Norway.; Department of Computer Science, UiT The Arctic University of Norway, Hansine Hansens veg 18, 9019, Tromsø, Norway.; Machine & Hybrid Intelligence Lab, Department of Radiology, Northwestern University, Chicago, USA., Ghatwary N; Computer Engineering Department, Arab Academy for Science and Technology,Smart Village, Giza, Egypt., Realdon S; Oncological Gastroenterology - Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 2, 33081, Aviano, PN, Italy., Cannizzaro R; Oncological Gastroenterology - Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 2, 33081, Aviano, PN, Italy.; Department of Medical, Surgical and Health Sciences, University of Trieste, 34127, Trieste, Italy., Salem OE; Faculty of Medicine, University of Alexandria, 21131, Alexandria, Egypt., Lamarque D; Université de Versailles St-Quentin en Yvelines, Hôpital Ambroise Paré, 9 Av. Charles de Gaulle, 92100, Boulogne-Billancourt, France., Daul C; CRAN UMR 7039, Université de Lorraine and CNRS, F-54010, Vandœuvre-Lès-Nancy, France., Riegler MA; SimulaMet, Pilestredet 52, 0167, Oslo, Norway.; Department of Computer Science, UiT The Arctic University of Norway, Hansine Hansens veg 18, 9019, Tromsø, Norway., Anonsen KV; Oslo University Hospital Ullevål, Kirkeveien 166, 0450, Oslo, Norway., Petlund A; Augere Medical, Nedre Vaskegang 6, 0186, Oslo, Norway., Halvorsen P; SimulaMet, Pilestredet 52, 0167, Oslo, Norway.; Oslo Metropolitan University, Pilestredet 46, 0167, Oslo, Norway., Rittscher J; Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, OX3 7DQ, Oxford, United Kingdom.; Oxford National Institute for Health Research Biomedical Research centre, OX4 2PG, Oxford, United Kingdom., de Lange T; Augere Medical, Nedre Vaskegang 6, 0186, Oslo, Norway.; Medical Department, Sahlgrenska University Hospital-Mölndal, Blå stråket 5, 413 45, Göteborg, Sweden.; Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, 41345, Göteborg, Sweden., East JE; Oxford National Institute for Health Research Biomedical Research centre, OX4 2PG, Oxford, United Kingdom.; Translational Gastroenterology Unit, Experimental Medicine Div., John Radcliffe Hospital, University of Oxford, OX3 9DU, Oxford, United Kingdom. |
المصدر: | Scientific data [Sci Data] 2023 Feb 06; Vol. 10 (1), pp. 75. Date of Electronic Publication: 2023 Feb 06. |
نوع المنشور: | Dataset; Journal Article |
اللغة: | English |
بيانات الدورية: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101640192 Publication Model: Electronic Cited Medium: Internet ISSN: 2052-4463 (Electronic) Linking ISSN: 20524463 NLM ISO Abbreviation: Sci Data Subsets: MEDLINE |
أسماء مطبوعة: | Original Publication: London : Nature Publishing Group, 2014- |
مواضيع طبية MeSH: | Colonic Neoplasms* , Colonic Polyps*/diagnosis, Humans ; Colonoscopy/methods |
مستخلص: | 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. (© 2023. The Author(s).) |
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تواريخ الأحداث: | Date Created: 20230206 Date Completed: 20230209 Latest Revision: 20230308 |
رمز التحديث: | 20240628 |
مُعرف محوري في PubMed: | PMC9902556 |
DOI: | 10.1038/s41597-023-01981-y |
PMID: | 36746950 |
قاعدة البيانات: | MEDLINE |
تدمد: | 2052-4463 |
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DOI: | 10.1038/s41597-023-01981-y |