Kvasir-SEG: A Segmented Polyp Dataset

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.

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Introduced in the Paper:

Kvasir-SEG Kvasir-Sessile dataset

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Polyp Segmentation Kvasir-SEG ResUNet DSC 0.7877 # 1
Medical Image Segmentation Kvasir-SEG ResUNet mean Dice 0.7877 # 40


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