Toward Quantitative Disease Assessment from Capsule Endoscopy Images

描述（由申请人提供）：胶囊式内窥镜最近已成为胃肠道（GI）的一种有价值的成像技术，尤其是小肠和食管。有了这项技术，它已经成为可能直接评估患者的肠道粘膜与各种条件，如不明原因的胃肠道出血，腹腔疾病和克罗恩病。虽然胶囊内镜的使用正在迅速增加，但胶囊内镜图像的评价提出了许多实际挑战。在典型的情况下，胶囊在8小时内获取50，000或更多的图像。这些图像的质量是高度可变的，这是由于胶囊本身在其移动通过胃肠道时的不受控制的运动、被成像的结构的复杂性以及成像器本身的固有限制。在实践中，这些图像中相对较少（通常少于100个）包含重要的诊断内容。因此，创造一种有效的、可重复的方法来评价胶囊内窥镜序列是具有挑战性的。该项目的目标是创建一个工具，用于半自动化，客观，定量评估胶囊内镜数据中的病理结果。临床重点将是定量评估出现在克罗恩病的小肠病变。该问题的技术方法将利用统计学习方法来创建以与训练有素的专家一致的方式执行病变分类和评估的算法。本项目的基本假设是，适当构建的算法将能够对胶囊内窥镜图像中出现的病变进行评估，其一致性水平与人类观察者相当。为证明这一假设，拟议项目将追求以下三个具体目标： 目标1：数据采集。建立肠道病变图像的实质性数据库，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。开发使用统计学习技术从图像内容对组织类型进行低级别分类的算法，并创建用于配准病变的多个部分视图以创建更完整视图的算法。 目标3：自动化病变评估。应用和验证统计学习方法，这些方法可以以与目标1中汇编的专家评估一致的方式评估目标2产生的图像。 R21的重点是开发在代表性数据库上证明有效的工具。这将为随后的技术发展奠定基础，从而实现病变的自动检测，并为随后的临床研究奠定基础，这些临床研究将在更实质性的临床环境中开发克罗恩病严重程度的定量测量方法。

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.