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Efficient Video Indexing for Monitoring Disease Activity and Progression in the Upper Gastrointestinal Tract

机译：用于监控上消化道疾病活动和进展的有效视频索引

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摘要

Endoscopy is a routine imaging technique used for both diagnosis and minimally invasive surgical treatment. While the endoscopy video contains a wealth of information, tools to capture this information for the purpose of clinical reporting are rather poor. In date, endoscopists do not have any access to tools that enable them to browse the video data in an efficient and user friendly manner. Fast and reliable video retrieval methods could for example, allow them to review data from previous exams and therefore improve their ability to monitor disease progression. Deep learning provides new avenues of compressing and indexing video in an extremely efficient manner. In this study, we propose to use an autoencoder for efficient video compression and fast retrieval of video images. To boost the accuracy of video image retrieval and to address data variability like multi-modality and view-point changes, we propose the integration of a Siamese network. We demonstrate that our approach is competitive in retrieving images from 3 large scale videos of 3 different patients obtained against the query samples of their previous diagnosis. Quantitative validation shows that the combined approach yield an overall improvement of 5% and 8% over classical and variational autoencoders, respectively.
机译：内窥镜检查是用于诊断和微创手术治疗的常规成像技术。虽然内窥镜检查视频包含大量信息，但是用于临床报告目的捕获该信息的工具却很差。迄今为止，内窥镜医师无法使用任何使他们能够以有效且用户友好的方式浏览视频数据的工具。例如，快速可靠的视频检索方法可以使他们查看以前检查的数据，从而提高他们监测疾病进展的能力。深度学习提供了以极其有效的方式对视频进行压缩和索引的新途径。在这项研究中，我们建议使用自动编码器进行有效的视频压缩和视频图像的快速检索。为了提高视频图像检索的准确性并解决诸如多模态和视点变化之类的数据可变性，我们提出了一个暹罗网络的集成方案。我们证明了我们的方法在检索来自3个不同患者的3个大规模视频的图像（相对于他们先前诊断的查询样本）中具有竞争力。定量验证表明，与传统和变体自动编码器相比，组合方法分别分别提高了5％和8％。

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