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Automatic Quantification of Crypt Architecture in Ex Vivo Gastrointestinal Epithelium for High-Resolution Microendoscopic Images

机译:高分辨率胃肠内镜图像在体外胃肠道上皮中的隐窝结构的自动定量

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High-resolution microendoscopy based on the fiber bundle has been showing immense potential to early detection of precancerous and cancerous lesions in gastrointestinal epithelium, especially for low-resource areas in China. However, obtaining clinical benefit from microendoscopic diagnosis usually remains in the hands of experts. Quantitative analysis focusing on computer-aided detection is therefore receiving attention as an attractive tool. In this paper, we present an automatic quantification method of crypts in gastrointestinal epithelium for high-resolution microendoscopic images, which is composed of four modules: filtering, contrast enhancement, crypt segmentation and morphologic quantification of crypts. The preliminary experiments on ex vivo image data indicate that the proposed method is effective for crypt segmentation from microendoscopic images with low-contrast, and quantitation of well-defined clinical features, which has a potential in future computer-aided diagnostic systems by revealing the morphologic characteristics of crypts at various clinical stages. The proposed method also enables instant processing. Thus, it may be a powerful tool for assisting endoscopists in real-time interpretation of high-resolution microendoscopic images, with high accuracy and consistent diagnosis. Furthermore, we are testing the method on larger gastrointestinal epithelium images and in vivo high-resolution microendoscopic images, and will integrate this work into a computer-aided diagnostic system.
机译:基于纤维束的高分辨率显微内窥镜检查显示出巨大潜力,可及早发现胃肠道上皮中癌前和癌前病变,尤其是在中国资源贫乏地区。然而,从显微内窥镜诊断中获得临床利益通常仍在专家的手中。因此,专注于计算机辅助检测的定量分析作为一种有吸引力的工具而受到关注。在本文中,我们提出了一种用于高分辨率显微内窥镜图像的胃肠道上皮隐窝的自动量化方法,该方法由四个模块组成:滤波,对比度增强,隐窝分割和隐窝的形态学量化。对离体图像数据的初步实验表明,该方法可有效地从低对比度的显微内窥镜图像中进行隐窝分割,并定量定义明确的临床特征,通过揭示形态学特征,在未来的计算机辅助诊断系统中具有潜力在各个临床阶段隐窝的特征。所提出的方法还使即时处理成为可能。因此,它可能是协助内窥镜医师实时解释高分辨率显微内窥镜图像的功能强大的工具,具有高精度和一致的诊断能力。此外,我们正在较大的胃肠道上皮图像和体内高分辨率显微内窥镜图像上测试该方法,并将这项工作整合到计算机辅助诊断系统中。

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