  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.
