Authors | D. Jha, P. H. Smedsrud, M. Riegler, P. Halvorsen, H. D. Johansen, T. de Lange, and D. Johansen |
Title | Kvasir-SEG: A Segmented Polyp Dataset |
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Status | Published |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | International Conference on Multimedia Modeling |
Pagination | 451-462 |
Publisher | Springer |
Place Published | Daejeon, Korea |
Keywords | Kvasir-SEG dataset, Medical images, Polyp segmentation, ResUNet Fuzzy c-mean clustering, Semantic segmentation |
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. |
Citation Key | 26811 |
