Department of Electrical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands; Department of Gastroenterology, Academic Medical Center, Postbus 22660, 1100 DD Amsterdam, The Netherlands.
Department of Electrical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands.
Comput Med Imaging Graph. 2018 Jul;67:9-20. doi: 10.1016/j.compmedimag.2018.02.007. Epub 2018 Apr 13.
The incidence of Barrett cancer is increasing rapidly and current screening protocols often miss the disease at an early, treatable stage. Volumetric Laser Endomicroscopy (VLE) is a promising new tool for finding this type of cancer early, capturing a full circumferential scan of Barrett's Esophagus (BE), up to 3-mm depth. However, the interpretation of these VLE scans can be complicated, due to the large amount of cross-sectional images and the subtle grayscale variations. Therefore, algorithms for automated analysis of VLE data can offer a valuable contribution to its overall interpretation. In this study, we broadly investigate the potential of Computer-Aided Detection (CADe) for the identification of early Barrett's cancer using VLE. We employ a histopathologically validated set of ex-vivo VLE images for evaluating and comparing a considerable set of widely-used image features and machine learning algorithms. In addition, we show that incorporating clinical knowledge in feature design, leads to a superior classification performance and additional benefits, such as low complexity and fast computation time. Furthermore, we identify an optimal tissue depth for classification of 0.5-1.0 mm, and propose an extension to the evaluated features that exploits this phenomenon, improving their predictive properties for cancer detection in VLE data. Finally, we compare the performance of the CADe methods with the classification accuracy of two VLE experts. With a maximum Area Under the Curve (AUC) in the range of 0.90-0.93 for the evaluated features and machine learning methods versus an AUC of 0.81 for the medical experts, our experiments show that computer-aided methods can achieve a considerably better performance than trained human observers in the analysis of VLE data.
巴雷特癌症的发病率正在迅速上升，而目前的筛查方案往往会错过早期可治疗阶段的疾病。容积激光内窥镜检查（VLE）是一种很有前途的新工具，可早期发现这种癌症，对巴雷特食管（BE）进行完整的圆周扫描，深度可达 3 毫米。然而，由于横截面图像数量大，灰度变化细微，这些 VLE 扫描的解释可能会变得复杂。因此，用于自动分析 VLE 数据的算法可以为其整体解释提供有价值的贡献。在这项研究中，我们广泛研究了使用 VLE 进行计算机辅助检测（CADe）以识别早期巴雷特癌症的潜力。我们使用一组经过组织病理学验证的离体 VLE 图像，来评估和比较广泛使用的图像特征和机器学习算法的相当多的组合。此外，我们表明，在特征设计中纳入临床知识可导致更好的分类性能和其他优势，例如低复杂性和快速计算时间。此外，我们确定了 0.5-1.0mm 的最佳组织深度用于分类，并提出了对评估特征的扩展，这可以提高其在 VLE 数据中癌症检测的预测性能。最后，我们将 CADe 方法的性能与两位 VLE 专家的分类准确性进行了比较。在所评估的特征和机器学习方法的 AUC 值范围为 0.90-0.93，而医学专家的 AUC 值为 0.81 时，我们的实验表明，计算机辅助方法在分析 VLE 数据方面可以比受过训练的人类观察者取得更好的性能。
