Kvasir-SEG: A Segmented Polyp Dataset
Jha, Debesh; Pia H, Smedsrud; Riegler, Michael; Halvorsen, Pål; de Lange, Thomas; Johansen, Dag; Johansen, Håvard D.
Peer reviewed, Journal article
Accepted version
Permanent lenke
https://hdl.handle.net/11250/3005709Utgivelsesdato
2020Metadata
Vis full innførselOriginalversjon
Lecture Notes in Computer Science (LNCS). 2020, 11962, 451-462. 10.1007/978-3-030-37734-2_37Sammendrag
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 by
a medical doctor and then verified by an experienced gastroenterologist.
Moreover, we also generated the bounding boxes of the polyp regions with
the help of segmentation masks. We demonstrate the use of our dataset
with a traditional segmentation approach and a modern deep-learning
based Convolutional Neural Network (CNN) approach. The dataset will
be of value for researchers to reproduce results and compare methods.
By adding segmentation masks to the Kvasir dataset, which only provide
frame-wise annotations, we enable multimedia and computer vision researchers to contribute in the field of polyp segmentation and automatic
analysis of colonoscopy images.