Quantitative analysis of patients with celiac disease by video capsule endoscopy: A deep learning method
Quantitative analysis of patients with celiac disease by video capsule endoscopy: A deep learning method
复制标题
通过视频胶囊内窥镜对乳糜泻患者进行定量分析：一种深度学习方法
DOI：
10.1016/j.compbiomed.2017.03.031
复制
发表时间：
2017-06-01
影响因子：
7.7
通讯作者：
Qin, Jing
中科院分区：
文献类型：
--
作者：
Zhou, Teng;Han, Guoqiang;Qin, Jing
背景乳糜泻是世界上最常见的疾病之一。胶囊式内窥镜是对整个小肠进行可视化的替代方法，而不会对患者造成创伤。这对描述乳糜泻是有用的，但是手动分析单个患者的回顾性数据需要数小时。通过深度学习方法进行的计算机辅助定量分析有助于减轻回顾性视频分析期间的工作量。对来自6名乳糜泻患者和5名对照者的胶囊式内窥镜剪辑进行预处理以用于训练。通过使用预选算法自动去除具有大视野的不透明腔外流体或气泡的帧。然后，在所提出的新方法中，在帧旋转之前对帧进行裁剪并校正强度。GoogLeNet是用这些框架训练的。然后，使用来自另外5名乳糜泻患者和另外5名对照患者的胶囊式内窥镜的夹子进行测试。经过训练的GoogLeNet能够区分乳糜泻患者与对照组胶囊式内窥镜剪辑中的帧。定量测量与置信度的评估，以评估在受试者的病理严重程度。依靠评估置信度，GoogLeNet对测试集实现了100%的灵敏度和特异性。t检验证实评价置信度对于区分乳糜泻患者与对照是显著的。此外，小肠黏膜病变的严重程度也可能与评估可信度有关。建立了一种深度卷积神经网络，用于定量测量整个小肠中病理的存在和程度，这可能会改善计算机辅助临床技术，以通过视频胶囊内窥镜实时评估粘膜萎缩和其他病因。
Background. Celiac disease is one of the most common diseases in the world. Capsule endoscopy is an alternative way to visualize the entire small intestine without invasiveness to the patient. It is useful to characterize celiac disease, but hours are need to manually analyze the retrospective data of a single patient. Computer-aided quantitative analysis by a deep learning method helps in alleviating the workload during analysis of the retrospective videos.Method. Capsule endoscopy clips from 6 celiac disease patients and 5 controls were preprocessed for training. The frames with a large field of opaque extraluminal fluid or air bubbles were removed automatically by using a pre-selection algorithm. Then the frames were cropped and the intensity was corrected prior to frame rotation in the proposed new method. The GoogLeNet is trained with these frames. Then, the clips of capsule endoscopy from 5 additional celiac disease patients and 5 additional control patients are used for testing. The trained GoogLeNet was able to distinguish the frames from capsule endoscopy clips of celiac disease patients vs controls. Quantitative measurement with evaluation of the confidence was developed to assess the severity level of pathology in the subjects.Results. Relying on the evaluation confidence, the GoogLeNet achieved 100% sensitivity and specificity for the testing set. The t-test confirmed the evaluation confidence is significant to distinguish celiac disease patients from controls. Furthermore, it is found that the evaluation confidence may also relate to the severity level of small bowel mucosal lesions.Conclusions. A deep convolutional neural network was established for quantitative measurement of the existence and degree of pathology throughout the small intestine, which may improve computer-aided clinical techniques to assess mucosal atrophy and other etiologies in real-time with videocapsule endoscopy.