COLON: The largest COlonoscopy LONg sequence public database (2403.00663v1)
Abstract: Colorectal cancer is the third most aggressive cancer worldwide. Polyps, as the main biomarker of the disease, are detected, localized, and characterized through colonoscopy procedures. Nonetheless, during the examination, up to 25% of polyps are missed, because of challenging conditions (camera movements, lighting changes), and the close similarity of polyps and intestinal folds. Besides, there is a remarked subjectivity and expert dependency to observe and detect abnormal regions along the intestinal tract. Currently, publicly available polyp datasets have allowed significant advances in computational strategies dedicated to characterizing non-parametric polyp shapes. These computational strategies have achieved remarkable scores of up to 90% in segmentation tasks. Nonetheless, these strategies operate on cropped and expert-selected frames that always observe polyps. In consequence, these computational approximations are far from clinical scenarios and real applications, where colonoscopies are redundant on intestinal background with high textural variability. In fact, the polyps typically represent less than 1% of total observations in a complete colonoscopy record. This work introduces COLON: the largest COlonoscopy LONg sequence dataset with around of 30 thousand polyp labeled frames and 400 thousand background frames. The dataset was collected from a total of 30 complete colonoscopies with polyps at different stages, variations in preparation procedures, and some cases the observation of surgical instrumentation. Additionally, 10 full intestinal background video control colonoscopies were integrated in order to achieve a robust polyp-background frame differentiation. The COLON dataset is open to the scientific community to bring new scenarios to propose computational tools dedicated to polyp detection and segmentation over long sequences, being closer to real colonoscopy scenarios.
- Jacques Ferlay et al., “Cancer statistics for the year 2020: An overview,” International journal of cancer, vol. 149, no. 4, pp. 778–789, 2021.
- Quentin Angermann et al., “Active learning for real time detection of polyps in videocolonoscopy,” Procedia Computer Science, vol. 90, pp. 182–187, 2016.
- Eduardo Perez, Gastroenterologa, McGraw Hill Mexico, 2012.
- Deng-Ping Fan et al., “Pranet: Parallel reverse attention network for polyp segmentation,” in MICCAI. Springer, 2020, pp. 263–273.
- Hemin Ali Qadir et al., “Toward real-time polyp detection using fully cnns for 2d gaussian shapes prediction,” Medical Image Analysis, vol. 68, pp. 101897, 2021.
- Robin Zachariah et al., “Prediction of polyp pathology using convolutional neural networks achieves ‘resect and discard’thresholds,” The American journal of gastroenterology, vol. 115, no. 1, pp. 138, 2020.
- Nima Tajbakhsh et al., “Automated polyp detection in colonoscopy videos using shape and context information,” IEEE transactions on medical imaging, vol. 35, no. 2, pp. 630–644, 2015.
- Quentin Angermann et al., “Towards real-time polyp detection in colonoscopy videos: Adapting still frame-based methodologies for video sequences analysis,” in CARE 2017, pp. 29–41. Springer, 2017.
- Jorge Bernal et al., “Polyp detection benchmark in colonoscopy videos using gtcreator: A novel fully configurable tool for easy and fast annotation of image databases,” in Proceedings of 32nd CARS conference, 2018.
- Jorge Bernal et al., “Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians,” Computerized medical imaging and graphics, vol. 43, pp. 99–111, 2015.
- Juan Silva et al., “Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer,” International journal of computer assisted radiology and surgery, vol. 9, no. 2, pp. 283–293, 2014.
- Jorge Bernal et al., “Towards automatic polyp detection with a polyp appearance model,” Pattern Recognition, vol. 45, no. 9, pp. 3166–3182, 2012.
- Cristina Sánchez-Montes et al., “Computer-aided prediction of polyp histology on white light colonoscopy using surface pattern analysis,” Endoscopy, vol. 51, no. 03, pp. 261–265, 2019.
- Jorge Bernal et al., “Gtcreator: a flexible annotation tool for image-based datasets,” International journal of computer assisted radiology and surgery, vol. 14, no. 2, pp. 191–201, 2019.
- Konstantin Pogorelov et al., “Kvasir: A multi-class image dataset for computer aided gastrointestinal disease detection,” in Proceedings of the 8th ACM on Multimedia Systems Conference, 2017, pp. 164–169.
- Chien-Hsiang Huang et al., “Hardnet-mseg: A simple encoder-decoder polyp segmentation neural network that achieves over 0.9 mean dice and 86 fps,” arXiv preprint arXiv:2101.07172, 2021.
- “Endoscopic recognition and classification of colorectal polyps,” Gastrointestinal Endoscopy Clinics, vol. 32, no. 2, pp. 227–240, 2022.
- “Cost savings in colonoscopy with artificial intelligence-aided polyp diagnosis: an add-on analysis of a clinical trial (with video),” Gastrointestinal endoscopy, vol. 92, no. 4, pp. 905–911, 2020.
- “Resunet++: An advanced architecture for medical image segmentation,” in 2019 IEEE international symposium on multimedia (ISM). IEEE, 2019, pp. 225–2255.
- “Weakly supervised polyp segmentation from an attention receptive field mechanism,” in EMBC. IEEE, 2022, pp. 3745–3748.