Ye Menglong, Johns Edward, Giannarou Stamatia, Yang Guang-Zhong
Med Image Comput Comput Assist Interv. 2014;17(Pt 2):316-23. doi: 10.1007/978-3-319-10470-6_40.
Endoscopic surveillance is a widely used method for monitoring abnormal changes in the gastrointestinal tract such as Barrett's esophagus. Direct visual assessment, however, is both time consuming and error prone, as it involves manual labelling of abnormalities on a large set of images. To assist surveillance, this paper proposes an online scene association scheme to summarise an endoscopic video into scenes, on-the-fly. This provides scene clustering based on visual contents, and also facilitates topological localisation during navigation. The proposed method is based on tracking and detection of visual landmarks on the tissue surface. A generative model is proposed for online learning of pairwise geometrical relationships between landmarks. This enables robust detection of landmarks and scene association under tissue deformation. Detailed experimental comparison and validation have been conducted on in vivo endoscopic videos to demonstrate the practical value of our approach.
内镜监测是一种广泛用于监测胃肠道异常变化（如巴雷特食管）的方法。然而，直接视觉评估既耗时又容易出错，因为它涉及在大量图像上手动标记异常。为了辅助监测，本文提出了一种在线场景关联方案，用于即时将内镜视频总结为场景。这提供了基于视觉内容的场景聚类，并且在导航过程中便于进行拓扑定位。所提出的方法基于对组织表面视觉标志物的跟踪和检测。提出了一种生成模型，用于在线学习标志物之间的成对几何关系。这使得在组织变形情况下能够可靠地检测标志物和进行场景关联。已对体内内镜视频进行了详细的实验比较和验证，以证明我们方法的实用价值。
