Endomapper dataset of complete calibrated endoscopy procedures

Azagra, Pablo (Universidad de Zaragoza) ; Sostres, Carlos (Universidad de Zaragoza) ; Ferrández, Ángel (Universidad de Zaragoza) ; Riazuelo, Luis (Universidad de Zaragoza) ; Tomasini, Clara (Universidad de Zaragoza) ; Barbed, O. León (Universidad de Zaragoza) ; Morlana, Javier (Universidad de Zaragoza) ; Recasens, David (Universidad de Zaragoza) ; Batlle Martínez, Víctor (Universidad de Zaragoza) ; Gómez-Rodríguez, Juan J. (Universidad de Zaragoza) ; Elvira, Richard (Universidad de Zaragoza) ; López, Julia ; Oriol, Cristina ; Civera, Javier (Universidad de Zaragoza) ; Tardós, Juan D. (Universidad de Zaragoza) ; Murillo, Ana C. (Universidad de Zaragoza) ; Lanas, Angel (Universidad de Zaragoza) ; Montiel, José M. M. (Universidad de Zaragoza)

Resumen: Computer-assisted systems are becoming broadly used in medicine. In endoscopy, most research focuses on the automatic detection of polyps or other pathologies, but localization and navigation of the endoscope are completely performed manually by physicians. To broaden this research and bring spatial Artificial Intelligence to endoscopies, data from complete procedures is needed. This paper introduces the Endomapper dataset, the first collection of complete endoscopy sequences acquired during regular medical practice, making secondary use of medical data. Its main purpose is to facilitate the development and evaluation of Visual Simultaneous Localization and Mapping (VSLAM) methods in real endoscopy data. The dataset contains more than 24 hours of video. It is the first endoscopic dataset that includes endoscope calibration as well as the original calibration videos. Meta-data and annotations associated with the dataset vary from the anatomical landmarks, procedure labeling, segmentations, reconstructions, simulated sequences with ground truth and same patient procedures. The software used in this paper is publicly available.

Idioma: Inglés

DOI: 10.1038/s41597-023-02564-7

Año: 2023

Publicado en: SCIENTIFIC DATA 10, 1 (2023), 671 [16 pp.]
ISSN: 2052-4463

Factor impacto JCR: 5.8 (2023)

Categ. JCR: MULTIDISCIPLINARY SCIENCES rank: 16 / 134 = 0.119 (2023) - Q1 - T1

Factor impacto CITESCORE: 11.2 - Education (Q1) - Library and Information Sciences (Q1) - Statistics, Probability and Uncertainty (Q1) - Computer Science Applications (Q1) - Statistics and Probability (Q1) - Information Systems (Q1)

Factor impacto SCIMAGO: 1.937 - Computer Science Applications (Q1) - Education (Q1) - Statistics, Probability and Uncertainty (Q1) - Library and Information Sciences (Q1) - Statistics and Probability (Q1) - Information Systems (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA/T45-23R
Financiación: info:eu-repo/grantAgreement/EC/H2020/863146/EU/EndoMapper: Real-time mapping from endoscopic video/EndoMapper
Financiación: info:eu-repo/grantAgreement/ES/MCIU/FPU20-06782

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Ingen.Sistemas y Automát. (Dpto. Informát.Ingenie.Sistms.)
Área (Departamento): Area Medicina (Dpto. Medicina, Psiqu. y Derm.)
Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)