Inflammation of the gastrointestinal (GI) tract accompanies several diseases, including Crohn’s disease. Currently, video capsule endoscopy and deep bowel enteroscopy are the main means for direct visualisation of the bowel surface. However, the use of optical imaging limits visualisation to the luminal surface only, which makes early-stage diagnosis difficult. In this study, we propose a learning enabled microultrasound ( $\mu $ US) system that aims to classify inflamed and non-inflamed bowel tissues. $\mu $ US images of the caecum, small bowel and colon were obtained from mice treated with agents to induce inflammation. Those images were then used to train three deep learning networks and to provide a ground truth of inflammation status. The classification accuracy was evaluated using 10-fold evaluation and additional B-scan images. Our deep learning approach allowed robust differentiation between healthy tissue and tissue with early signs of inflammation that is not detectable by current endoscopic methods or by human inspection of the $\mu $ US images. The methods may be a foundation for future early GI disease diagnosis and enhanced management with computer-aided imaging.

中文翻译：

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一种基于学习的微超声系统，用于检测胃肠道的炎症。

胃肠道（GI）的炎症伴有多种疾病，包括克罗恩氏病。当前，视频胶囊内窥镜检查和深肠小肠镜检查是直接可视化肠表面的主要手段。然而，光学成像的使用将可视化仅限于腔表面，这使得早期诊断变得困难。在这项研究中，我们提出了一种具有学习功能的微超声波（ $ \亩$ 美国）系统，旨在对发炎和未发炎的肠组织进行分类。 $ \亩$ 盲肠，小肠和结肠的US图像是从用药物诱导炎症的小鼠中获得的。然后，这些图像被用于训练三个深度学习网络并提供炎症状态的基本信息。使用10倍评估和其他B扫描图像评估分类准确性。我们的深度学习方法允许健康组织和具有炎症早期迹象的组织之间的强大区分，而目前的内窥镜检查方法或人工检查无法检测到炎症的早期迹象 $ \亩$ 美国图像。该方法可能为将来早期胃肠道疾病的诊断和计算机辅助成像的增强管理奠定基础。