Jiang Yang, Gong Yuanzheng, Rubenstein Joel H, Wang Thomas D, Seibel Eric J
University of Washington, Department of Bioengineering, Human Photonics Lab, Seattle, Washington, United States.
University of Washington, Department of Mechanical Engineering, Human Photonics Lab, Seattle, Washington, United States.
J Med Imaging (Bellingham). 2017 Apr;4(2):024502. doi: 10.1117/1.JMI.4.2.024502. Epub 2017 May 24.
Multimodal endoscopy using fluorescence molecular probes is a promising method of surveying the entire esophagus to detect cancer progression. Using the fluorescence ratio of a target compared to a surrounding background, a quantitative value is diagnostic for progression from Barrett's esophagus to high-grade dysplasia (HGD) and esophageal adenocarcinoma (EAC). However, current quantification of fluorescent images is done only after the endoscopic procedure. We developed a Chan-Vese-based algorithm to segment fluorescence targets, and subsequent morphological operations to generate background, thus calculating target/background (T/B) ratios, potentially to provide real-time guidance for biopsy and endoscopic therapy. With an initial processing speed of 2 fps and by calculating the T/B ratio for each frame, our method provides quasireal-time quantification of the molecular probe labeling to the endoscopist. Furthermore, an automatic computer-aided diagnosis algorithm can be applied to the recorded endoscopic video, and the overall T/B ratio is calculated for each patient. The receiver operating characteristic curve was employed to determine the threshold for classification of HGD/EAC using leave-one-out cross-validation. With 92% sensitivity and 75% specificity to classify HGD/EAC, our automatic algorithm shows promising results for a surveillance procedure to help manage esophageal cancer and other cancers inspected by endoscopy.
使用荧光分子探针的多模态内镜检查是一种有望对整个食管进行检测以发现癌症进展的方法。利用目标与周围背景的荧光比率,一个定量值可用于诊断从巴雷特食管进展到高级别异型增生(HGD)和食管腺癌(EAC)。然而,目前荧光图像的定量仅在内镜检查后进行。我们开发了一种基于Chan-Vese的算法来分割荧光目标,并通过后续的形态学操作生成背景,从而计算目标/背景(T/B)比率,有可能为活检和内镜治疗提供实时指导。我们的方法初始处理速度为每秒2帧,并通过计算每一帧的T/B比率,为准实时地向内镜医师提供分子探针标记的定量结果。此外,可将自动计算机辅助诊断算法应用于记录的内镜视频,并为每位患者计算总体T/B比率。采用留一法交叉验证,利用受试者工作特征曲线来确定HGD/EAC分类的阈值。我们的自动算法对HGD/EAC分类的灵敏度为92%,特异性为75%,在用于帮助管理食管癌和其他内镜检查的癌症的监测程序中显示出有前景的结果。