AuthorsD. Jha, P. H. Smedsrud, M. Riegler, P. Halvorsen, H. D. Johansen, T. de Lange, and D. Johansen
TitleKvasir-SEG: A Segmented Polyp Dataset
AfilliationMachine Learning
Project(s)Department of Holistic Systems
Publication TypeProceedings, refereed
Year of Publication2020
Conference NameInternational Conference on Multimedia Modeling
Place PublishedDaejeon, Korea
KeywordsKvasir-SEG dataset, Medical images, Polyp segmentation, ResUNet Fuzzy c-mean clustering, Semantic segmentation

Pixel-wise image segmentation is a highly demanding task in medical image analysis. In practice, it is difficult to find annotated medical images with corresponding segmentation masks. In this paper, we present  Kvasir-SEG: an open-access dataset of gastrointestinal polyp images and corresponding segmentation masks, manually annotated and verified by an experienced gastroenterologist. We demonstrate the use of our dataset with a traditional segmentation approach and a modern deep learning based CNN approach. This work will be valuable for researchers to reproduce results and compare their methods in the future. By adding segmentation masks to the Kvasir dataset, which until today only consisted of framewise annotations, we enable multimedia and computer vision researchers to contribute in the field of polyp segmentation and automatic analysis of colonoscopy videos.  

Citation Key26811

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