首页|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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To support probe-based confocal laser endomicroscopy (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 offline 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 135lesions 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-outcross-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.

Colorectal neoplasiaComputer-aided diagnosisContent-based image retrievalNearest neighbor classification softwareProbe-based confocal laser endomicroscopy

Barbara André、Tom Vercauteren、Anna M Buchner、Murli Krishna、Nicholas Ayache、Michael B Wallace

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Image Computing Group,Mauna Kea Technologies, 75010 Paris, France

Asclepios Research Team,The National Institute for Research in Computer Science and Control Sophia Antipolis, 06902 Sophia Antipolis, France

Division of Gastroenterology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, United States

Laboratory Medicine and Pathology, Mayo Clinic Hospital in Jacksonville, Jacksonville, FL 3224, United States

Department of Gastroenterology and Hepatology, Mayo Clinic Hospital in Jacksonville, Jacksonville, FL 3224, United States

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2012

10.3748/wjg.v18.i39.5560

世界胃肠病学杂志(英文版)
太原消化病研治中心

世界胃肠病学杂志(英文版)

SCI
影响因子：1.001
ISSN：1007-9327
年,卷(期)：2012.18(39)
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