通过 Medico 2020 与 MedAI 2021 挑战赛验证结肠镜检查中的息肉和器械分割方法
Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges
AI总结：
本文通过组织 Medico 2020 和 MedAI 2021 两项竞赛，系统评估并总结了结肠镜息肉与手术器械分割方法，强调算法准确性、透明性及可复现性，并探讨其临床转化前景。
摘要：
结肠镜检查图像的自动分析一直是一个活跃的研究领域，其动力来源于对癌前息肉早期检测重要性的认识。然而，在实时检查过程中检测息肉可能具有挑战性，原因包括内镜医师之间技能和经验差异、注意力不足以及疲劳等多种因素，这些因素导致较高的息肉漏检率。深度学习已成为应对这一挑战的有前景的解决方案，因为它可以实时辅助内镜医师检测和分类被忽视的息肉和异常。除了算法的准确性之外，透明性和可解释性对于解释算法预测的“为什么”和“如何”也至关重要。此外，大多数算法是在私有数据、闭源代码或专有软件中开发的，方法缺乏可复现性。因此，为了促进高效且透明方法的开发，我们组织了“Medico 自动息肉分割（Medico 2020）”和“MedAI：医学图像分割中的透明性（MedAI 2021）”竞赛。我们呈现了一份全面的总结并分析了每项参赛成果，突出了表现最佳方法的优势，并讨论了此类方法向临床转化的可能性。对于透明性任务，一个包括胃肠病学专家在内的多学科团队评估了每份提交成果，并基于开源实践、失败案例分析、消融研究、评估的可用性和可理解性对各团队进行评价，以更深入地了解模型在临床部署中的可信度。通过对挑战赛的全面分析，我们不仅突出了息肉和手术器械分割方面的进展，还鼓励进行定性评估，以构建更透明、更易理解的基于人工智能的结肠镜检查系统。
英文摘要：
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.
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