Intelligent Image Feature Matching for Small Intestine Capsule Endoscopy

描述(申请人提供)：小肠疾病影响了近1900万美国人。通过最近引入的“胶囊内窥镜”，这些患者的护理得到了显著改善。这包括吞下一个药丸形状的成像设备，该设备将图像无线传输到外部接收器。这是第一次实现了对小肠粘膜的非侵入性视觉成像。这项技术有可能为30%-50%的隐匿性胃肠道出血患者提供诊断，即使在食道胃十二指肠镜(EGD)和结肠镜检查后仍无法解释。然而，胶囊内窥镜检查涉及手动审查大约50,000张由该设备拍摄的图像。此外，由于胶囊的运动性未知，在图像中看到的特定病变的定位是具有挑战性的。这促使我们小组研究使用视觉运动跟踪来加强胶囊内窥镜检查研究的回顾。我们建议从单个胶囊图像创建单个重建的小肠图像表示。这将提供三个关键优势：1)图像将被合并，以消除对相同区域的肠腔的多余检查，从而减少总检查时间。2)通过创建图像之间的空间关系，病变定位将成为可能；以及3)如果图像不重叠，我们将能够识别未成像的区域，而目前的胶囊内窥镜系统无法做到这一点。我们的第一阶段工作包括开发智能图像特征匹配软件，并使用胶囊内窥镜平台的图像进行评估。我们将评估1)特征识别，2)图像之间的特征匹配，以及3)基于捕获的胶囊内窥镜图像测量腔内距离的能力。如果成功，我们设想第二阶段的工作将利用我们的智能特征匹配技术来构建先进的GI图像浏览系统。我们建议从单个胶囊图像创建小肠的单一马赛克表示。该系统将在一项试验性的人体研究中进行评估，以调查其快速可视化和定位潜在病变的能力。我们的长期目标是为胃肠科医生提供一种非侵入性的小肠可视化工具，该工具能够快速解释并允许准确定位病变。胶囊内窥镜首次实现了对远端小肠粘膜的非侵入性视觉成像。然而，它受到耗时的手动审查和无法定位病变的阻碍。我们提议的软件技术将为胃肠病专家提供一种快速有效的工具来审查胶囊内窥镜检查数据。

DESCRIPTION (provided by applicant): Diseases of the small intestine affect nearly 19 million Americans. The care of these patients has improved significantly via the recent introduction of "capsule endoscopy". This involves swallowing a pill-shaped imaging device which wirelessly transmits images to an external receiver. For the first time, this has enabled non-invasive visual imaging of the small intestinal mucosa. The technology has the potential to provide a diagnosis to the 30 - 50 % of patients whose occult gastrointestinal bleeding remains unexplained even after thorough workup with esophagogastroduodenoscopy (EGD) and colonoscopy. However, capsule endoscopy involves manual review of approximately 50,000 images taken by the device. Furthermore, localization of a particular lesion seen in the images is challenging due to the unknown motility of the capsule. This prompted our group to investigate the use of visual motion tracking to enhance review of capsule endoscopy studies. We propose to create a single reconstructed image representation of the small intestine from the individual capsule images. This will provide three key advantages: 1) Images will be combined to eliminate redundant review of the same area of the intestinal lumen, thereby decreasing total review time. 2) By creating spatial relations among images, lesion localization will be possible; and 3) If images are non-overlapping, we will be able to identify unimaged areas, which current capsule endoscopy systems cannot do. Our Phase I effort involves development of the intelligent image feature matching software and evaluation using images from a capsule endoscopy platform. We will evaluate 1) feature identification, 2) feature matching between images, and 3) the ability to measure intraluminal distances based on the captured images of the capsule endoscope. If successful, we envision a Phase II effort that will harness our intelligent feature matching technology to construct an advanced GI image browsing system. We propose to create a single mosaiced representation of the small bowel from the individual capsule images. The system will be evaluated in a pilot human study to investigate its ability to quickly visualize and localize potential lesions. It is our long term goal to provide gastroenterologists with a non-invasive tool for visualization of the small intestine which is quick to interpret and allows accurate localization of lesions. Capsule endoscopy for the first time has enabled non-invasive visual imaging of the distal small intestinal mucosa. However, it is hindered by time-consuming manual reviews and an inability to localize lesions. Our proposed software technology will provide gastroenterologists with a quick and efficient tool to review capsule endoscopy data.