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在线跟踪和重定向及其在胃肠内窥镜检查光学活检中的应用。

Online tracking and retargeting with applications to optical biopsy in gastrointestinal endoscopic examinations.

机构信息

The Hamlyn Centre for Robotic Surgery, Imperial College London, United Kingdom.

The Hamlyn Centre for Robotic Surgery, Imperial College London, United Kingdom.

出版信息

Med Image Anal. 2016 May;30:144-157. doi: 10.1016/j.media.2015.10.003. Epub 2015 Oct 19.

Abstract

With recent advances in biophotonics, techniques such as narrow band imaging, confocal laser endomicroscopy, fluorescence spectroscopy, and optical coherence tomography, can be combined with normal white-light endoscopes to provide in vivo microscopic tissue characterisation, potentially avoiding the need for offline histological analysis. Despite the advantages of these techniques to provide online optical biopsy in situ, it is challenging for gastroenterologists to retarget the optical biopsy sites during endoscopic examinations. This is because optical biopsy does not leave any mark on the tissue. Furthermore, typical endoscopic cameras only have a limited field-of-view and the biopsy sites often enter or exit the camera view as the endoscope moves. In this paper, a framework for online tracking and retargeting is proposed based on the concept of tracking-by-detection. An online detection cascade is proposed where a random binary descriptor using Haar-like features is included as a random forest classifier. For robust retargeting, we have also proposed a RANSAC-based location verification component that incorporates shape context. The proposed detection cascade can be readily integrated with other temporal trackers. Detailed performance evaluation on in vivo gastrointestinal video sequences demonstrates the performance advantage of the proposed method over the current state-of-the-art.

摘要

随着生物光子学的最新进展,诸如窄带成像、共聚焦激光内窥镜检查、荧光光谱和光相干断层扫描等技术可以与普通白光内窥镜相结合,提供体内微观组织特征,可能避免离线组织学分析的需要。尽管这些技术具有提供在线光学活检的优势,但对于胃肠病学家来说,在内窥镜检查期间重新定位光学活检部位具有挑战性。这是因为光学活检不会在组织上留下任何痕迹。此外,典型的内窥镜相机只有有限的视场,并且当内窥镜移动时,活检部位经常进入或离开相机视野。在本文中,提出了一种基于跟踪检测概念的在线跟踪和重新定位框架。提出了一个在线检测级联,其中包括使用哈尔特征的随机二进制描述符作为随机森林分类器。为了进行稳健的重新定位,我们还提出了一种基于 RANSAC 的位置验证组件,该组件包含形状上下文。所提出的检测级联可以很容易地与其他时间跟踪器集成。在体内胃肠道视频序列上的详细性能评估表明,与当前最先进的方法相比,所提出的方法具有性能优势。

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