Pixel-wise Wireless Capsule Endoscopy Image Annotated Dataset for Clear and Contaminated Region Segmentation

Published: 15 December 2023| Version 2 | DOI: 10.17632/vmxhn95j8z.2
vahid sadeghi, Alireza Mehridehnavi, Yasaman Sanahmadi, Mohsen Sharifi


The first publicly available clear and contaminated regions segmentation mask dataset created by precisely annotating 1500 copyright-free CC BY 4.0 licensed small bowel capsule endoscopy images collected from Kvasir capsule endoscopy dataset [1]. The created dataset has been organized into three subfolders namely original images, binary masks, and tri-color masks. The original images folder contains 1500 handpicked wireless capsule endoscopy images covering different level of contamination. The binary masks folder contains a binary ground truth segmentation mask for each individual image of the original images folder. In a black-and –white segmentation mask image, white pixels represent clear regions while contaminated regions have been indexed by black pixels. Considering the physiological meaning of bubbles and turbid fluids, the tri-color masks folder contain three-color manually annotated ground truth masks in which the bubble boundaries, turbid fluids, and clear tissue have been labeled by the blue, red, and white colors. Ground truth images in the binary masks, and tri-color masks folders share the same names as the raw images in the original images folder. In the provided dataset, original RGB color images, binary and tri-color masks have been converted into three distinct .npy file formats. These files contain the pixel intensity values of the image, black-and-white and three-color masks. These reduced storages files can be used to carry out image processing tasks more lightly. [1] P. H. Smedsrud et al., “Kvasir-Capsule, a video capsule endoscopy dataset,” Sci. Data, vol. 8, no. 1, pp. 1–10, 2021, doi: 10.1038/s41597-021-00920-z.



Isfahan University of Medical Sciences


Artificial Intelligence, Computer Vision, Gastroenterology, Image Processing, Image Segmentation, Bioelectrical Engineering, Capsule Endoscopy, Video Summarization, Image Analysis