Journal Article

Kvasir-Capsule, a video capsule endoscopy dataset

Næss, Espen, Lux, Mathias, Schmidt, Peter T., Eskeland, Sigrun L., Toth, Ervin, Riegler, Michael A., Nedrejord, Oda Olsen, Borgli, Hanna, Halvorsen, Pål, Gjestang, Henrik, Jha, Debesh, Petlund, Andreas, Thambawita, Vajira, Johansen, Dag, Smedsrud, Pia H., Espeland, Håvard, Berstad, Tor Jan Derek, Hicks, Steven A., Nguyen, Duc Tien Dang, Hammer, Hugo L., de Lange, Thomas, Garcia-Ceja, Enrique

2021

Overview

Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. Existing work demonstrates the promising benefits of AI-based computer-assisted diagnosis systems for VCE. They also show great potential for improvements to achieve even better results. Also, medical data is often sparse and unavailable to the research community, and qualified medical personnel rarely have time for the tedious labelling work. We present Kvasir-Capsule , a large VCE dataset collected from examinations at a Norwegian Hospital. Kvasir-Capsule consists of 117 videos which can be used to extract a total of 4,741,504 image frames. We have labelled and medically verified 47,238 frames with a bounding box around findings from 14 different classes. In addition to these labelled images, there are 4,694,266 unlabelled frames included in the dataset. The Kvasir-Capsule dataset can play a valuable role in developing better algorithms in order to reach true potential of VCE technology. Measurement(s) Gastrointestinal Tract • gastrointestinal system disease Technology Type(s) Capsule Endoscope • visual assessment of in vivo video recording Sample Characteristic - Organism Homo sapiens Sample Characteristic - Environment alimentary part of gastrointestinal system Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14178905

• Publisher: Nature Publishing Group UK,Nature Portfolio
• Subject: 692/699/1503/197 / 692/700/139/422 / Annan medicin och hälsovetenskap / artificial-intelligence / classification / Clinical Medicine / Data Descriptor / diseases / Gastroenterologi och hepatologi / Gastroenterology and Hepatology / Humanities and Social Sciences / interobserver agreement / Klinisk medicin / Medical and Health Sciences / Medicin och hälsovetenskap / multidisciplinary / Other Medical and Health Sciences / polyp / Science / Science & Technology - Other Topics / Science (multidisciplinary)