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.

翻译：摘要：结直肠癌是全球第三大侵袭性癌症。息肉作为该疾病的主要生物标志物，通过结肠镜检查进行检测、定位和表征。然而，在检查过程中，由于挑战性条件（如摄像头移动、光照变化）以及息肉与肠道褶皱的高度相似性，高达25%的息肉可能被漏诊。此外，沿肠道观察和检测异常区域存在显著的主观性和专家依赖性。目前，公开可用的息肉数据集已显著推动了专注于表征非参数息肉形状的计算策略的发展。这些计算策略在分割任务中取得了高达90%的卓越得分。然而，这些策略仅在裁剪过的、由专家选择的始终包含息肉的帧上运行。因此，这些计算近似方法远未达到临床场景和实际应用的要求——实际结肠镜检查中，背景肠道组织具有高度纹理变异性，且息肉通常仅占完整结肠镜记录中总观察帧数的不到1%。本文介绍了COLON：目前最大的结肠镜长序列数据集，包含约3万张息肉标注帧和40万张背景帧。该数据集源自30例完整结肠镜检查，涵盖不同阶段的息肉、制备流程变异以及部分手术器械观察案例。此外，为增强息肉-背景帧的鲁棒性区分，整合了10例全肠道背景视频对照结肠镜检查。COLON数据集向科学界开放，旨在为长序列上的息肉检测与分割提供新场景，推动更贴近真实结肠镜环境计算工具的研发。