Image analysis for classification of dysplasia in Barrett's esophagus using endoscopic optical coherence tomography.
Image analysis for classification of dysplasia in Barrett's esophagus using endoscopic optical coherence tomography.
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DOI：
10.1364/boe.1.000825
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发表时间：
2010-09-09
影响因子：
3.4
通讯作者：
Rollins AM
中科院分区：
文献类型：
--
作者：
Qi X;Pan Y;Sivak MV;Willis JE;Isenberg G;Rollins AM
巴雷特食管（BE）和相关腺癌已成为一个主要的卫生保健问题。内窥镜光学相干断层扫描是一种显微镜下表面成像技术，已被证明可以区分胃肠道壁的组织层并识别粘膜中的异型增生，并被提议作为一种监测工具，以帮助管理BE。在这项工作中，一个计算机辅助诊断（CAD）系统已被证明是使用EOCT的Barrett食管异型增生的分类。该系统由四个模块组成：感兴趣区域分割、发育不良相关图像特征提取、特征选择和部位分类与验证。对多种特征提取和分类方法进行了评价，并详细描述了CAD系统的开发过程。还研究了使用多个EOCT图像对单个部位进行分类。使用CAD系统分析了来自先前描述的临床研究的总计96个EOCT图像-活检对（63个非异型增生、26个低度异型增生和7个高度异型增生活检部位），非异型增生与异型增生BE组织分类的准确性为84%。这些结果推动了CAD的持续发展，从而可能使EOCT监测Barrett粘膜的大表面积以识别发育不良。
Barrett’s esophagus (BE) and associated adenocarcinoma have emerged as a major health care problem. Endoscopic optical coherence tomography is a microscopic sub-surface imaging technology that has been shown to differentiate tissue layers of the gastrointestinal wall and identify dysplasia in the mucosa, and is proposed as a surveillance tool to aid in management of BE. In this work a computer-aided diagnosis (CAD) system has been demonstrated for classification of dysplasia in Barrett’s esophagus using EOCT. The system is composed of four modules: region of interest segmentation, dysplasia-related image feature extraction, feature selection, and site classification and validation. Multiple feature extraction and classification methods were evaluated and the process of developing the CAD system is described in detail. Use of multiple EOCT images to classify a single site was also investigated. A total of 96 EOCT image-biopsy pairs (63 non-dysplastic, 26 low-grade and 7 high-grade dysplastic biopsy sites) from a previously described clinical study were analyzed using the CAD system, yielding an accuracy of 84% for classification of non-dysplastic vs. dysplastic BE tissue. The results motivate continued development of CAD to potentially enable EOCT surveillance of large surface areas of Barrett’s mucosa to identify dysplasia.