Software for automated classification of probe-based confocal laser endomicroscopy videos of colorectal polyps
Software for automated classification of probe-based confocal laser endomicroscopy videos of colorectal polyps
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DOI：
10.3748/wjg.v18.i39.5560
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发表时间：
2012-10-21
影响因子：
4.3
通讯作者：
Wallace, Michael B.
中科院分区：
文献类型：
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作者：
Andre, Barbara;Vercauteren, Tom;Wallace, Michael B.
目的：为了支持探针为基础的共聚焦激光内镜（pCLE）诊断设计软件的结肠polyps.METHODS的自动分类：静脉荧光素pCLE成像结直肠病变进行筛选和监测结肠镜检查的患者，其次是息肉切除术。所有切除标本均由对pCLE信息不知情的参考胃肠道病理学家进行审查。组织学被用作区分肿瘤性和非肿瘤性病变的标准。由2名对内镜特征和组织病理学不知情的内镜专家离线分析每个息肉记录的pCLE视频序列。这些pCLE视频，沿着他们的组织病理学诊断，被用来训练自动分类软件，这是一个基于内容的图像检索技术，然后k-最近邻分类。由2位专家内窥镜医师建立的pCLE视频的离线诊断的性能与自动pCLE软件分类的性能进行了比较。所有的评价进行了留一病人交叉验证，以避免bias.Results：结直肠病变（135）成像71例。根据组织病理学，这135处病变中有93处为肿瘤性病变，42处为非肿瘤性病变。该研究发现，自动pCLE软件分类的性能之间的差异没有统计学意义，（准确性89.6%，灵敏度92.5%，特异性83.3%，使用留一患者交叉验证）和由2位专家内窥镜医师建立的pCLE视频的离线诊断的性能（准确性89.6%，灵敏度91.4%，特异性85.7%）。检测观察到的差异的功效非常低（< 6%）。等效性检验的95%置信区间为：准确性为-0.073至0.073，灵敏度为-0.068至0.089，特异性为-0.18至0.13。在这项研究中提出的分类软件不是一个“黑盒子”，而是一个信息工具，基于查询的例子模型，产生，作为中间结果，视觉上相似的注释视频，可直接由内窥镜医生解释。所提出的用于结肠息肉的pCLE视频的自动分类的软件实现了高性能，与由专家内窥镜医师建立的pCLE视频的离线诊断相当。(C)2012年百世登。All rights reserved.
AIM: To support probe-based confocal laser endonnicroscopy (pCLE) diagnosis by designing software for the automated classification of colonic polyps.METHODS: Intravenous fluorescein pCLE imaging of colorectal lesions was performed on patients undergoing screening and surveillance colonoscopies, followed by polypectomies. All resected specimens were reviewed by a reference gastrointestinal pathologist blinded to pCLE information. Histopathology was used as the criterion standard for the differentiation between neoplastic and non-neoplastic lesions. The pCLE video sequences, recorded for each polyp, were analyzed off-line by 2 expert endoscopists who were blinded to the endoscopic characteristics and histopathology. These pCLE videos, along with their histopathology diagnosis, were used to train the automated classification software which is a content-based image retrieval technique followed by k-nearest neighbor classification. The performance of the off-line diagnosis of pCLE videos established by the 2 expert endoscopists was compared with that of automated pCLE software classification. All evaluations were performed using leave-one-patient-out cross-validation to avoid bias.RESULTS: Colorectal lesions (135) were imaged in 71 patients. Based on histopathology, 93 of these 135 lesions were neoplastic and 42 were non-neoplastic. The study found no statistical significance for the difference between the performance of automated pCLE software classification (accuracy 89.6%, sensitivity 92.5%, specificity 83.3%, using leave-one-patient-out cross-validation) and the performance of the off-line diagnosis of pCLE videos established by the 2 expert endoscopists (accuracy 89.6%, sensitivity 91.4%, specificity 85.7%). There was very low power (< 6%) to detect the observed differences. The 95% confidence intervals for equivalence testing were: -0.073 to 0.073 for accuracy, -0.068 to 0.089 for sensitivity and -0.18 to 0.13 for specificity. The classification software proposed in this study is not a "black box" but an informative tool based on the query by example model that produces, as intermediate results, visually similar annotated videos that are directly interpretable by the endoscopist.CONCLUSION: The proposed software for automated classification of pCLE videos of colonic polyps achieves high performance, comparable to that of off-line diagnosis of pCLE videos established by expert endoscopists. (C) 2012 Baishideng. All rights reserved.