A complete benchmark for polyp detection, segmentation and classification in colonoscopy images
IntroductionColorectal 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 as...
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Frontiers Media S.A.
2024-09-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2024.1417862/full |
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| author | Yael Tudela Mireia Majó Neil de la Fuente Adrian Galdran Adrian Krenzer Frank Puppe Amine Yamlahi Thuy Nuong Tran Bogdan J. Matuszewski Kerr Fitzgerald Cheng Bian Junwen Pan Shijle Liu Gloria Fernández-Esparrach Aymeric Histace Jorge Bernal |
| author_facet | Yael Tudela Mireia Majó Neil de la Fuente Adrian Galdran Adrian Krenzer Frank Puppe Amine Yamlahi Thuy Nuong Tran Bogdan J. Matuszewski Kerr Fitzgerald Cheng Bian Junwen Pan Shijle Liu Gloria Fernández-Esparrach Aymeric Histace Jorge Bernal |
| author_sort | Yael Tudela |
| collection | DOAJ |
| description | IntroductionColorectal 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.MethodsThis study presents a complete validation framework and we compare several methodologies for each of the polyp characterization tasks.ResultsResults 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.DiscussionWhile 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. |
| format | Article |
| id | doaj-art-059a0add1a84450499dc90757c953842 |
| institution | OA Journals |
| issn | 2234-943X |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-059a0add1a84450499dc90757c9538422025-08-20T01:54:30ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2024-09-011410.3389/fonc.2024.14178621417862A complete benchmark for polyp detection, segmentation and classification in colonoscopy imagesYael Tudela0Mireia Majó1Neil de la Fuente2Adrian Galdran3Adrian Krenzer4Frank Puppe5Amine Yamlahi6Thuy Nuong Tran7Bogdan J. Matuszewski8Kerr Fitzgerald9Cheng Bian10Junwen Pan11Shijle Liu12Gloria Fernández-Esparrach13Aymeric Histace14Jorge Bernal15Computer Vision Center and Computer Science Department, Universitat Autònoma de Cerdanyola del Valles, Barcelona, SpainComputer Vision Center and Computer Science Department, Universitat Autònoma de Cerdanyola del Valles, Barcelona, SpainComputer Vision Center and Computer Science Department, Universitat Autònoma de Cerdanyola del Valles, Barcelona, SpainDepartment of Information and Communication Technologies, SymBioSys Research Group, BCNMedTech, Barcelona, SpainArtificial Intelligence and Knowledge Systems, Institute for Computer Science, Julius-Maximilians University of Würzburg, Würzburg, GermanyArtificial Intelligence and Knowledge Systems, Institute for Computer Science, Julius-Maximilians University of Würzburg, Würzburg, GermanyDivision of Intelligent Medical Systems, German Cancer Research Center (DKFZ), Heidelberg, GermanyDivision of Intelligent Medical Systems, German Cancer Research Center (DKFZ), Heidelberg, GermanyComputer Vision and Machine Learning (CVML) Research Group, University of Central Lancashir (UCLan), Preston, United KingdomComputer Vision and Machine Learning (CVML) Research Group, University of Central Lancashir (UCLan), Preston, United KingdomHebei University of Technology, Baoding, ChinaTianjin University, Tianjin, ChinaHebei University of Technology, Baoding, ChinaDigestive Endoscopy Unit, Hospital Clínic, Barcelona, SpainETIS UMR 8051, École Nationale Supérieure de l'Électronique et de ses Applications (ENSEA), Centre national de la recherche scientifique (CNRS), CY Paris Cergy University, Cergy, FranceComputer Vision Center and Computer Science Department, Universitat Autònoma de Cerdanyola del Valles, Barcelona, SpainIntroductionColorectal 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.MethodsThis study presents a complete validation framework and we compare several methodologies for each of the polyp characterization tasks.ResultsResults 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.DiscussionWhile 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.https://www.frontiersin.org/articles/10.3389/fonc.2024.1417862/fullcomputer-aided diagnosismedical imagingpolyp classificationpolyp detectionpolyp segmentation |
| spellingShingle | Yael Tudela Mireia Majó Neil de la Fuente Adrian Galdran Adrian Krenzer Frank Puppe Amine Yamlahi Thuy Nuong Tran Bogdan J. Matuszewski Kerr Fitzgerald Cheng Bian Junwen Pan Shijle Liu Gloria Fernández-Esparrach Aymeric Histace Jorge Bernal A complete benchmark for polyp detection, segmentation and classification in colonoscopy images Frontiers in Oncology computer-aided diagnosis medical imaging polyp classification polyp detection polyp segmentation |
| title | A complete benchmark for polyp detection, segmentation and classification in colonoscopy images |
| title_full | A complete benchmark for polyp detection, segmentation and classification in colonoscopy images |
| title_fullStr | A complete benchmark for polyp detection, segmentation and classification in colonoscopy images |
| title_full_unstemmed | A complete benchmark for polyp detection, segmentation and classification in colonoscopy images |
| title_short | A complete benchmark for polyp detection, segmentation and classification in colonoscopy images |
| title_sort | complete benchmark for polyp detection segmentation and classification in colonoscopy images |
| topic | computer-aided diagnosis medical imaging polyp classification polyp detection polyp segmentation |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2024.1417862/full |
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