University of North Carolina at Chapel Hill, Chapel Hill, NC 27705, USA.
University of North Carolina at Chapel Hill, Chapel Hill, NC 27705, USA.
Med Image Anal. 2021 Aug;72:102100. doi: 10.1016/j.media.2021.102100. Epub 2021 May 19.
Colonoscopy is the gold standard for pre-cancerous polyps screening and treatment. The polyp detection rate is highly tied to the percentage of surveyed colonic surface. However, current colonoscopy technique cannot guarantee that all the colonic surface is well examined because of incomplete camera orientations and of occlusions. The missing regions can hardly be noticed in a continuous first-person perspective. Therefore, a useful contribution would be an automatic system that can compute missing regions from an endoscopic video in real-time and alert the endoscopists when a large missing region is detected. We present a novel method that reconstructs dense chunks of a 3D colon in real time, leaving the unsurveyed part unreconstructed. The method combines a standard SLAM system with a depth and pose prediction network to achieve much more robust tracking and less drift. It addresses the difficulties for colonoscopic images of existing simultaneous localization and mapping (SLAM) systems and end-to-end deep learning methods.
结肠镜检查是癌前息肉筛查和治疗的金标准。息肉检出率与被检查结肠表面的百分比密切相关。然而，由于摄像角度不完整和阻塞，目前的结肠镜检查技术不能保证所有的结肠表面都得到很好的检查。在连续的第一人称视角中，很难注意到缺失的区域。因此，一个有用的贡献是一个能够实时计算内窥镜视频中缺失区域的自动系统，并在检测到大面积缺失区域时向内窥镜医生发出警报。我们提出了一种新的方法，可以实时重建 3D 结肠的密集块，而不重建未被检查的部分。该方法将标准的 SLAM 系统与深度和姿态预测网络相结合，实现了更稳健的跟踪和更少的漂移。它解决了现有同时定位和映射 (SLAM) 系统和端到端深度学习方法对结肠镜图像的困难。
