University of Houston, Department of Biomedical Engineering, 3605 Cullen Boulevard, Houston, Texas 77204-5060, USA.
J Biomed Opt. 2014 Feb;19(2):21102. doi: 10.1117/1.JBO.19.2.021102.
We present a three-dimensional (3-D) computational method to detect soft tissue sarcomas with the goal of automatic surgical margin assessment based on optical coherence tomography (OCT) images. Three parameters are investigated and quantified from OCT images as the indicators for the tissue diagnosis including the signal attenuation (A-line slope), the standard deviation of the signal fluctuations (speckles), and the exponential decay coefficient of its spatial frequency spectrum. The detection of soft tissue sarcomas relies on the combination of these three parameters, which are related to the optical attenuation characteristics and the structural features of the tissue. Pilot experiments were performed on ex vivo human tissue samples with homogeneous pieces (both normal and abnormal) and tumor margins. Our results demonstrate the feasibility of this computational method in the differentiation of soft tissue sarcomas from normal tissues. The features of A-line-based detection and 3-D quantitative analysis yield promise for a computer-aided technique capable of accurately and automatically identifying resection margins of soft tissue sarcomas during surgical treatment.
我们提出了一种三维（3-D）计算方法，用于检测软组织肉瘤，旨在基于光学相干断层扫描（OCT）图像实现自动手术边界评估。从 OCT 图像中研究和量化了三个参数，作为组织诊断的指标，包括信号衰减（A 线斜率）、信号波动的标准差（散斑）和空间频谱的指数衰减系数。软组织肉瘤的检测依赖于这三个参数的组合，它们与组织的光衰减特性和结构特征有关。在具有均匀切片（正常和异常）和肿瘤边界的离体人组织样本上进行了初步实验。我们的结果表明，这种计算方法在区分软组织肉瘤与正常组织方面具有可行性。基于 A 线的检测和 3-D 定量分析的特点为一种计算机辅助技术提供了希望，该技术能够在手术治疗过程中准确且自动地识别软组织肉瘤的切除边界。
