Colonoscopy is the primary method for examination, detection, and removal of polyps. However, challenges such as variations among the endoscopists' skills, bowel quality preparation, and the complex nature of the large intestine contribute to high polyp miss-rate. These missed polyps can develop into cancer later, underscoring the importance of improving the detection methods. To address this gap of lack of publicly available, multi-center large and diverse datasets for developing automatic methods for polyp detection and segmentation, we introduce PolypDB, a large scale publicly available dataset that contains 3934 still polyp images and their corresponding ground truth from real colonoscopy videos. PolypDB comprises images from five modalities: Blue Light Imaging (BLI), Flexible Imaging Color Enhancement (FICE), Linked Color Imaging (LCI), Narrow Band Imaging (NBI), and White Light Imaging (WLI) from three medical centers in Norway, Sweden, and Vietnam. We provide a benchmark on each modality and center, including federated learning settings using popular segmentation and detection benchmarks. PolypDB is public and can be downloaded at \url{https://osf.io/pr7ms/}. More information about the dataset, segmentation, detection, federated learning benchmark and train-test split can be found at \url{https://github.com/DebeshJha/PolypDB}.
@article{arxiv.2409.00045,
title = {PolypDB: A Curated Multi-Center Dataset for Development of AI Algorithms in Colonoscopy},
author = {Debesh Jha and Nikhil Kumar Tomar and Vanshali Sharma and Quoc-Huy Trinh and Koushik Biswas and Hongyi Pan and Ritika K. Jha and Gorkem Durak and Alexander Hann and Jonas Varkey and Hang Viet Dao and Long Van Dao and Binh Phuc Nguyen and Nikolaos Papachrysos and Brandon Rieders and Peter Thelin Schmidt and Enrik Geissler and Tyler Berzin and Pål Halvorsen and Michael A. Riegler and Thomas de Lange and Ulas Bagci},
journal= {arXiv preprint arXiv:2409.00045},
year = {2025}
}