Toward Quantitative Disease Assessment from Capsule Endoscopy Images

描述(申请人提供)：胶囊内窥镜检查最近已经成为胃肠道(GI)的一种有价值的成像技术，特别是小肠和食道。有了这项技术，就有可能直接评估患有各种疾病的患者的肠道粘膜，如不明原因的胃肠道出血、乳糜泻和克罗恩病。尽管胶囊内窥镜的应用正在迅速增加，但胶囊内窥镜图像的评估提出了许多实际挑战。在典型情况下，太空舱在8小时内获取5万张或更多图像。这些图像的质量是高度可变的，这是由于胶囊本身在穿过胃肠道时不受控制的运动，被成像结构的复杂性，以及成像器本身的固有限制。实际上，这些图像中包含重要诊断内容的相对较少(通常不到100个)。因此，创造一种有效的、可重复的方法来评估胶囊内窥镜序列是具有挑战性的。该项目的目标是创建一种工具，用于半自动、客观、定量地评估胶囊内窥镜数据中的病理发现。临床的重点将是定量评估出现在克罗恩病的小肠病变。解决这一问题的技术方法将利用统计学习方法来创建算法，以与训练有素的专家一致的方式执行病变分类和评估。该项目的基本假设是，适当构建的算法将能够以与人类观察者相当的一致性水平对胶囊内窥镜图像中出现的病变进行评估。为了证明这一假设，拟议的项目将追求以下三个具体目标： 目标1：数据采集。开发一个实质性的肠道病变图像数据库，并对指示病变严重程度的几个属性进行专家评估。 目的2：组织分类和图像增强。利用统计学习技术开发用于根据图像内容对组织类型进行低级分类的算法，并创建用于配准病变的多个局部视图的算法以创建更完整的视图。 目标3：自动损伤评估。应用和验证能够以与目标1汇编的专家评估一致的方式评估目标2产生的图像的统计学习方法。 R21的重点是开发在具有代表性的数据语料库上证明有效的工具。这将为随后的技术发展以及随后的临床研究奠定基础，这些技术发展导致了病变的自动检测，并解决了在更实质性的临床环境中开发克隆氏病严重程度的定量测量的问题。

DESCRIPTION (provided by applicant): Capsule endoscopy has recently emerged as a valuable imaging technology for the gastrointestinal (GI) tract, especially the small bowel and the esophagus. With this technology, it has become possible to directly evaluate the gut mucosa of patients with a variety of conditions, such as obscure gastrointestinal bleeding, celiac disease and Crohn's disease. Although the use of capsule endoscopy is gaining rapidly, the evaluation of capsule endoscopic imagery presents numerous practical challenges. In a typical case, the capsule acquires 50,000 or more images over an eight-hour period. The quality of these images is highly variable due to the uncontrolled motion of the capsule itself as it moves through the GI tract, the complexity of the structures being imaged, and inherent limitations of the imager itself. In practice, relatively few (often less than 100) of these images contain significant diagnostic content. As a result, it is challenging to create an effective, repeatable means for evaluating capsule endoscopic sequences. The goal of this project is create a tool for semi-automated, objective, quantitative assessment of pathologic findings in capsule endoscopic data. The clinical focus will be on quantitative assessment of lesions that appear in Crohn's disease of the small bowel. The technical approach to this problem will make use of statistical learning methods to create algorithms that perform lesion classification and assessment in a manner consistent with a trained expert. The underlying hypothesis of this project is that appropriately constructed algorithms will be able to perform assessment of lesions appearing in capsule endoscopic images with a level of consistency comparable to human observers. In proving this hypothesis, the proposed project will pursue the following three specific aims: Aim 1: Data acquisition. To develop a substantial database of images of intestinal lesions together with an expert assessment of several attributes indicative of lesion severity. Aim 2: Tissue classification and image enhancement. To develop algorithms for low-level classification of tissue type from image content using statistical learning techniques, and to create algorithms for registering multiple partial views of a lesion to create more complete views. Aim 3: Automated Lesion Assessment. To apply and validate statistical learning methods that can assess the images produced by Aim 2 in a manner consistent with the expert assessments compiled in Aim 1. The focus of this R21 is on the development of tools that have proven efficacy on a representative corpus of data. This will set the stage for subsequent technological developments leading toward the automated detection of lesions, and subsequent clinical studies addressing the development of quantitative measures for Crohn's disease severity in a more substantial clinical setting.