@inproceedings {26811,
	title = {Kvasir-SEG: A Segmented Polyp Dataset},
	journal = {International Conference on Multimedia Modeling},
	year = {2020},
	pages = {451-462},
	publisher = {Springer},
	address = {Daejeon, Korea},
	abstract = {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\&nbsp; 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.\&nbsp;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.\&nbsp;\&nbsp;},
	keywords = {Kvasir-SEG dataset, Medical images, Polyp segmentation, ResUNet  Fuzzy c-mean clustering, Semantic segmentation},
	author = {Jha, Debesh and Pia H. {Smedsrud} and Riegler, Michael and Halvorsen, P{\r a}l and H{\r a}vard D. {Johansen} and de Lange, Thomas and Johansen, Dag}
}
