A complete benchmark for polyp detection, segmentation and classification in colonoscopy images

• Published in: Frontiers in oncology 2024-09, Vol.14, p.1417862
• Main Authors: Tudela, Yael, Majó, Mireia, de la Fuente, Neil, Galdran, Adrian, Krenzer, Adrian, Puppe, Frank, Yamlahi, Amine, Tran, Thuy Nuong, Matuszewski, Bogdan J, Fitzgerald, Kerr, Bian, Cheng, Pan, Junwen, Liu, Shijle, Fernández-Esparrach, Gloria, Histace, Aymeric, Bernal, Jorge
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Computer Science; computer-aided diagnosis; Human health and pathology; Hépatology and Gastroenterology; Life Sciences; medical imaging; Oncology; polyp classification; polyp detection; polyp segmentation
• ISSN: 2234-943X; 2234-943X
• DOI: 10.3389/fonc.2024.1417862

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.