Ciaccio Edward J, Tennyson Christina A, Bhagat Govind, Lewis Suzanne K, Green Peter H
Edward J Ciaccio, Christina A Tennyson, Govind Bhagat, Suzanne K Lewis, Peter H Green, Department of Medicine, Columbia University Medical Center, New York, NY 10032, United States.
World J Gastrointest Endosc. 2013 Jul 16;5(7):313-22. doi: 10.4253/wjge.v5.i7.313.
To investigate the presence of small intestinal villous atrophy in celiac disease patients from quantitative analysis of videocapsule image sequences.
Nine celiac patient data with biopsy-proven villous atrophy and seven control patient data lacking villous atrophy were used for analysis. Celiacs had biopsy-proven disease with scores of Marsh II-IIIC except in the case of one hemophiliac patient. At four small intestinal levels (duodenal bulb, distal duodenum, jejunum, and ileum), video clips of length 200 frames (100 s) were analyzed. Twenty-four measurements were used for image characterization. These measurements were determined by quantitatively processing the videocapsule images via techniques for texture analysis, motility estimation, volumetric reconstruction using shape-from-shading principles, and image transformation. Each automated measurement method, or automaton, was polled as to whether or not villous atrophy was present in the small intestine, indicating celiac disease. Each automaton's vote was determined based upon an optimized parameter threshold level, with the threshold levels being determined from prior data. A prediction of villous atrophy was made if it received the majority of votes (≥ 13), while no prediction was made for tie votes (12-12). Thus each set of images was classified as being from either a celiac disease patient or from a control patient.
Separated by intestinal level, the overall sensitivity of automata polling for predicting villous atrophy and hence celiac disease was 83.9%, while the specificity was 92.9%, and the overall accuracy of automata-based polling was 88.1%. The method of image transformation yielded the highest sensitivity at 93.8%, while the method of texture analysis using subbands had the highest specificity at 76.0%. Similar results of prediction were observed at all four small intestinal locations, but there were more tie votes at location 4 (ileum). Incorrect prediction which reduced sensitivity occurred for two celiac patients with Marsh type II pattern, which is characterized by crypt hyperplasia, but normal villous architecture. Pooled from all levels, there was a mean of 14.31 ± 3.28 automaton votes for celiac vs 9.67 ± 3.31 automaton votes for control when celiac patient data was analyzed (P < 0.001). Pooled from all levels, there was a mean of 9.71 ± 2.8128 automaton votes for celiac vs 14.32 ± 2.7931 automaton votes for control when control patient data was analyzed (P < 0.001).
Automata-based polling may be useful to indicate presence of mucosal atrophy, indicative of celiac disease, across the entire small bowel, though this must be confirmed in a larger patient set. Since the method is quantitative and automated, it can potentially eliminate observer bias and enable the detection of subtle abnormality in patients lacking a clear diagnosis. Our paradigm was found to be more efficacious at proximal small intestinal locations, which may suggest a greater presence and severity of villous atrophy at proximal as compared with distal locations.
通过对视频胶囊图像序列进行定量分析，研究乳糜泻患者小肠绒毛萎缩情况。
采用9例经活检证实有绒毛萎缩的乳糜泻患者数据和7例无绒毛萎缩的对照患者数据进行分析。除1例血友病患者外，乳糜泻患者经活检证实患有疾病，Marsh分级为II-IIIC级。在小肠的四个水平（十二指肠球部、十二指肠远端、空肠和回肠），分析长度为200帧（100秒）的视频片段。使用24项测量指标进行图像特征描述。这些测量指标通过纹理分析、运动估计、基于明暗恢复形状原理的体积重建以及图像变换等技术对视频胶囊图像进行定量处理来确定。针对每个自动测量方法（即自动机），询问小肠中是否存在绒毛萎缩，以指示乳糜泻。每个自动机的投票基于优化后的参数阈值水平确定，阈值水平根据先前数据确定。如果获得多数票（≥13票），则预测存在绒毛萎缩，而对于平局票（12-12票）则不做预测。因此，每组图像被分类为来自乳糜泻患者或对照患者。
按肠段划分，自动机投票预测绒毛萎缩进而诊断乳糜泻的总体敏感性为83.9%，特异性为92.9%，基于自动机投票的总体准确率为88.1%。图像变换方法的敏感性最高，为93.8%，而使用子带的纹理分析方法的特异性最高，为76.0%。在小肠的所有四个位置均观察到类似的预测结果，但在位置4（回肠）出现的平局票更多。两名具有Marsh II型模式（其特征为隐窝增生但绒毛结构正常）的乳糜泻患者出现了降低敏感性的错误预测。对所有水平的数据进行汇总分析，分析乳糜泻患者数据时，乳糜泻患者的自动机平均得票为14.31±3.28票，对照患者为9.67±3.31票（P<0.001）。分析对照患者数据时，乳糜泻患者的自动机平均得票为9.71±2.8128票，对照患者为14.32±2.7931票（P<0.001）。
基于自动机的投票可能有助于指示整个小肠中存在提示乳糜泻的黏膜萎缩，不过这必须在更大的患者群体中得到证实。由于该方法是定量且自动化的，它有可能消除观察者偏差，并能够检测出缺乏明确诊断的患者中的细微异常。我们的模式在小肠近端位置更有效，这可能表明与远端位置相比，近端绒毛萎缩的存在更多且更严重。
