A complete benchmark for polyp detection, segmentation and classification in colonoscopy images
Tudela, Yael (Universitat Autònoma de Barcelona. Departament de Ciències de la Computació)
Majó, Mireia (Universitat Autònoma de Barcelona. Departament de Ciències de la Computació)
de la Fuente, Neil (Universitat Autònoma de Barcelona. Departament de Ciències de la Computació)
Galdran, Adrian (SymBioSys Research Group, BCNMedTech)
Krenzer, Adrian (Julius-Maximilians-Universität Würzburg)
Puppe, Frank (Julius-Maximilians-Universität Würzburg)
Yamlahi, Amine (German Cancer Research Center (DKFZ))
Tran, Thuy Nuong (German Cancer Research Center (DKFZ))
Matuszewski, Bogdan J. (University of Central Lancashir)
Fitzgerald, Kerr (University of Central Lancashir)
Bian, Cheng (Hebei University of Technology)
Pan, Junwen (Tianjin University)
Liu, Shijle (Hebei University of Technology)
Fernández-Esparrach, Gloria (Hospital Clínic i Provincial de Barcelona)
Histace, Aymeric (CY Paris Cergy University)
Bernal del Nozal, Jorge (Universitat Autònoma de Barcelona. Departament de Ciències de la Computació)

Data:	2024
Resum:	Colorectal cancer (CRC) is one of the main causes of deaths worldwide. Early detection and diagnosis of its precursor lesion, the polyp, is key to reduce its mortality and to improve procedure efficiency. During the last two decades, several computational methods have been proposed to assist clinicians in detection, segmentation and classification tasks but the lack of a common public validation framework makes it difficult to determine which of them is ready to be deployed in the exploration room. This study presents a complete validation framework and we compare several methodologies for each of the polyp characterization tasks. Results show that the majority of the approaches are able to provide good performance for the detection and segmentation task, but that there is room for improvement regarding polyp classification. While studied show promising results in the assistance of polyp detection and segmentation tasks, further research should be done in classification task to obtain reliable results to assist the clinicians during the procedure. The presented framework provides a standarized method for evaluating and comparing different approaches, which could facilitate the identification of clinically prepared assisting methods.
Ajuts:	Agencia Estatal de Investigación PID2020-120311RB-I00 European Commission 892297 Ministerio de Ciencia e Innovación RED2022-134964-T
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Computer-aided diagnosis ; Medical imaging ; Polyp classification ; Polyp detection ; Polyp segmentation
Publicat a:	Frontiers in Oncology, Vol. 14 (September 2024) , art. 1417862, ISSN 2234-943X

DOI: 10.3389/fonc.2024.1417862
PMID: 39381041

19 p, 5.5 MB

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