Colonoscopy is the most widely used medical technique for preventing Colorectal Cancer, by detecting and removing polyps before they become malignant. Recent studies show that around one quarter of the existing polyps are routinely missed. While some of these do appear in the endoscopist's field of view, others are missed due to a partial coverage of the colon. The task of detecting and marking unseen regions of the colon has been addressed in recent work, where the common approach is based on dense 3D reconstruction, which proves to be challenging due to lack of 3D ground truth and periods with poor visual content. In this paper we propose a novel and complementary method to detect deficient local coverage in real-time for video segments where a reliable 3D reconstruction is impossible. Our method aims to identify skips along the colon caused by a drifted position of the endoscope during poor visibility time intervals. The proposed solution consists of two phases. During the first, time segments with good visibility of the colon and gaps between them are identified. During the second phase, a trained model operates on each gap, answering the question: Do you observe the same scene before and after the gap? If the answer is negative, the endoscopist is alerted and can be directed to the appropriate area in real-time. The second phase model is trained using a contrastive loss based on auto-generated examples. Our method evaluation on a dataset of 250 procedures annotated by trained physicians provides sensitivity of 0.75 with specificity of 0.9.

翻译：结肠镜检查是目前预防结直肠癌最广泛使用的医疗技术，通过检测并切除尚未恶变的息肉来预防癌症。最新研究表明，约四分之一的现有息肉在常规检查中被遗漏。虽然部分息肉确实出现在内镜医师视野中，但另一些则因结肠覆盖不全而被漏检。近期研究已着手解决结肠未观测区域的检测与标记问题，其通用方法基于密集三维重建，但由于缺乏三维真值及存在视觉内容贫乏时段，该方法颇具挑战性。本文提出一种新颖且互补的方法，可在无法进行可靠三维重建的视频片段中，实时检测局部覆盖不足区域。该方法旨在识别因内窥镜在低可见度时段位置漂移导致的结肠扫描跳跃。所提方案包含两个阶段：第一阶段识别结肠可见度良好的时段及其间的间隙；第二阶段由训练模型对每个间隙进行判定，回答"间隙前后是否观察到相同场景"的问题。若答案为否定，则实时警示内镜医师并引导其前往相应区域。第二阶段模型采用基于自动生成样本的对比损失函数进行训练。我们在包含250例操作（由执业医师标注）的数据集上对方法进行评估，获得了灵敏度0.75、特异度0.9的结果。