Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Deep learning has emerged as a promising solution to this challenge as it can assist endoscopists in detecting and classifying overlooked polyps and abnormalities in real time. In addition to the algorithm's accuracy, transparency and interpretability are crucial to explaining the whys and hows of the algorithm's prediction. Further, most algorithms are developed in private data, closed source, or proprietary software, and methods lack reproducibility. Therefore, to promote the development of efficient and transparent methods, we have organized the "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image Segmentation (MedAI 2021)" competitions. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic. For the transparency task, a multi-disciplinary team, including expert gastroenterologists, accessed each submission and evaluated the team based on open-source practices, failure case analysis, ablation studies, usability and understandability of evaluations to gain a deeper understanding of the models' credibility for clinical deployment. Through the comprehensive analysis of the challenge, we not only highlight the advancements in polyp and surgical instrument segmentation but also encourage qualitative evaluation for building more transparent and understandable AI-based colonoscopy systems.

翻译：结肠镜图像的自动分析一直是活跃的研究领域，其驱动力源于早期检测癌前息肉的重要性。然而，由于内镜医师技能与经验差异、注意力不足及疲劳等因素，实时检查中息肉检测仍面临挑战，导致较高的息肉漏检率。深度学习已成为解决该难题的有前景方案，能够辅助内镜医师实时检测并分类被忽视的息肉和异常。除算法准确性外，透明性与可解释性对于阐明算法预测的"为何"与"如何"至关重要。此外，多数算法基于私有数据、闭源代码或专有软件开发，方法缺乏可重复性。为此，我们组织了"Medico自动息肉分割（Medico 2020）"与"MedAI：医学图像分割中的透明度（MedAI 2021）"竞赛，以推动高效透明方法的发展。我们提供全面总结并分析每项贡献，强调最优方法的优势，探讨此类方法临床转化的可能性。针对透明度任务，由消化内科专家参与的多学科团队评估了每项提交，基于开源实践、失败案例分析、消融研究、评估的可用性与可理解性，深入理解模型用于临床部署的可信度。通过竞赛的全面分析，我们不仅凸显了息肉与手术器械分割的进展，还鼓励通过定性评估构建更透明、可理解的AI结肠镜系统。