Annotated Capsule Endoscopy Datasets for Clean and Contaminated Regions Segmentation: CECleanliness, Kvasir, SEE-AI, and Corrupted Kvasir

This dataset accompanies the research article titled "Multivariate Gaussian Bayes Classifier with Limited Data for Segmentation of Clean and Contaminated Regions in Small Bowel Capsule Endoscopy Images," submitted to PLOS ONE. The dataset includes four separate files:
CECleanliness Database: A collection of original capsule endoscopy images and their corresponding binary ground truth masks, annotated for clean and contaminated regions segmentation in the small bowel.Kvasir Capsule Endoscopy Dataset: A subset of images from the Kvasir dataset, curated for capsule endoscopy studies, with binary masks for segmenting clean and contaminated regions.SEE-AI Project Dataset: Data from the SEE-AI project, containing capsule endoscopy images with expert-annotated ground truth masks, focusing on regions of contamination.Corrupted Kvasir Dataset: A dataset of images derived from the Kvasir capsule endoscopy collection, including corrupted or lower-quality images, alongside their annotated masks.Each image in these datasets is paired with a corresponding binary mask that labels clean and contaminated regions, providing essential data for training and evaluating segmentation models. These datasets were utilized to develop and validate the proposed Gaussian Bayes classifier method for clean-contaminated segmentation in capsule endoscopy images, particularly under limited data conditions.
The dataset is intended to support further research in the field of capsule endoscopy, segmentation, and contamination detection, facilitating the development of automated tools for improving clinical outcomes in gastroenterology.

本数据集配套于投稿至《PLOS ONE》的研究论文《面向小肠胶囊内镜图像清洁与污染区域分割的有限数据多元高斯贝叶斯分类器》。
本数据集包含四个独立文件：
1. CE清洁度数据库（CECleanliness Database）：收录原始胶囊内镜（capsule endoscopy）图像及其对应的二值真值掩码，用于标注小肠内清洁与污染区域的分割任务。
2. Kvasir胶囊内镜数据集（Kvasir Capsule Endoscopy Dataset）：源自Kvasir数据集的图像子集，专为胶囊内镜研究整理，带有用于分割清洁与污染区域的二值掩码。
3. SEE-AI项目数据集（SEE-AI Project Dataset）：来自SEE-AI项目的数据，包含带有专家标注真值掩码的胶囊内镜图像，聚焦于污染区域的分割。
4. 带噪Kvasir数据集（Corrupted Kvasir Dataset）：源自Kvasir胶囊内镜数据集的图像集合，包含低质量或损坏的图像及其标注掩码。
上述数据集中的每张图像均配有对应的二值掩码，用于标注清洁与污染区域，为分割模型的训练与评估提供了核心支撑数据。本数据集被用于开发并验证所提出的有限数据条件下的多元高斯贝叶斯分类器方法，以实现胶囊内镜图像的清洁-污染区域分割任务。
本数据集旨在支持胶囊内镜、图像分割及污染检测领域的后续研究，助力开发自动化工具以改善胃肠病学的临床诊疗结局。