Pixel-wise Annotation for Clear and Contaminated Regions Segmentation in Wireless Capsule Endoscopy Images: A Multicentre Database

The first publicly available clear and contaminated regions segmentation mask multicentre dataset created by precisely annotating 17593 copyright-free CC BY 4.0 licensed small bowel capsule endoscopy images collected from Kvasir capsule endoscopy dataset (1), SEE-AI project database (2), and CECleanliness dataset (3) . The provided dataset consists of 4 main folders available in the data repository, namely "Kvasir capsule endoscopy dataset", "SEE-AI project database", "CECleanliness dataset", and "Combination". In the "Combination" folder, there are four subfolders namely "Images", "Gastroenterologist 1"," Gastroenterologist 2", and " Gastroenterologist 3". The "Images" subfolder includes the randomly selected 153 images from the pool of raw Kvasir, SEE-AI, and CECleanliness datasets. The three remaining subfolders contain the corresponding created masks of the randomly chosen images by the three gastroenterologists. Each one of the "Kvasir capsule endoscopy dataset", "SEE-AI project database", and "CECleanliness dataset" folders contain "Images", "Binary GT", "Tri-colour GT", and " Score" subfolders. In each subfolder, the "Images" contains raw frames from the related capsule endoscopy dataset. The "Binary GT" 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-colour GT" 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. The "Score" subfolder in each folder includes an Excel file in which the amount of clear area in each image and its cleanliness level has been reported. 1. Smedsrud PH, Thambawita V, Hicks SA, Gjestang H, Nedrejord OO, Næss E, et al. Kvasir-Capsule, a video capsule endoscopy dataset. Sci Data. 2021;8(1):1–10. 2. Yokote A, Umeno J, Kawasaki K, Fujioka S, Fuyuno Y, Matsuno Y, et al. Small bowel capsule endoscopy examination and open access database with artificial intelligence: The SEE‐artificial intelligence project. DEN Open. 2024;4(1):1–10. 3. Noorda R, Nevárez A, Colomer A, Pons Beltrán V, Naranjo V. Automatic evaluation of degree of cleanliness in capsule endoscopy based on a novel CNN architecture. Sci Rep [Internet]. 2020;10(1):1–13. Available from: https://doi.org/10.1038/s41598-020-74668-8