@misc{15455,
  keywords = {Medical images, Polyp segmentation, Semantic segmentation, Kvasir-SEG dataset, ResUNet  Fuzzy c-mean clustering},
  author = {Debesh Jha and Pia Smedsrud and Michael Riegler and P{\r a}l Halvorsen and H{\r a}vard Johansen and Thomas de Lange and Dag Johansen},
  title = {Kvasir-SEG: A Segmented Polyp Dataset},
  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  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.},
  year = {2020},
  journal = {International Conference on Multimedia Modeling},
  pages = {451-462},
  publisher = {Springer},
  address = {Daejeon, Korea},
}