论文标题
内窥镜视频的灌注定量：学习阅读肿瘤特征
Perfusion Quantification from Endoscopic Videos: Learning to Read Tumor Signatures
论文作者
论文摘要
在荧光引导性癌症手术中，恶性与良性或健康组织的术中鉴定是一个主要挑战。我们提出了一种用于对动态灌注模式细微差异的计算机辅助解释的灌注定量方法，该方法可用于通过使用多光谱内镜视频实时术中正常组织和良性或恶性肿瘤区分。该方法利用了这样一个事实，即由癌症血管生成引起的脉管系统使肿瘤与周围组织的灌注模式不同，并定义了肿瘤的特征，该肿瘤可用于将肿瘤与正常组织区分开。我们在大肠癌外科手术群体中对我们方法的实验评估表明，拟议的肿瘤特征能够以95％的精度成功区分健康，癌和良性组织。
Intra-operative identification of malignant versus benign or healthy tissue is a major challenge in fluorescence guided cancer surgery. We propose a perfusion quantification method for computer-aided interpretation of subtle differences in dynamic perfusion patterns which can be used to distinguish between normal tissue and benign or malignant tumors intra-operatively in real-time by using multispectral endoscopic videos. The method exploits the fact that vasculature arising from cancer angiogenesis gives tumors differing perfusion patterns from the surrounding tissue, and defines a signature of tumor which could be used to differentiate tumors from normal tissues. Experimental evaluation of our method on a cohort of colorectal cancer surgery endoscopic videos suggests that the proposed tumor signature is able to successfully discriminate between healthy, cancerous and benign tissue with 95% accuracy.
