Abstract Colorectal cancer (CRC) is one of the main alimentary tract system malignancies affecting people worldwide. Adenomatous polyps are precursors of CRC, and therefore, preventing the development of these lesions may also prevent subsequent malignancy. However, the adenoma detection rate (ADR), a measure of the ability of a colonoscopist to identify and remove precancerous colorectal polyps, varies significantly among endoscopists. Here, we attempt to use a convolutional neural network (CNN) to generate a unique computer-aided diagnosis (CAD) system by exploring in detail the multiple-scale performance of deep neural networks. We applied this system to 3,375 hand-labeled images from the screening colonoscopies of 1,197 patients; of whom, 3,045 were assigned to the training dataset and 330 to the testing dataset. The images were diagnosed simply as either an adenomatous or non-adenomatous polyp. When applied to the testing dataset, our CNN-CAD system achieved a mean average precision of 89.5%. We conclude that the proposed framework could increase the ADR and decrease the incidence of interval CRCs, although further validation through large multicenter trials is required.

中文翻译：

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使用Mask R-CNN在结肠镜检查过程中自动检测和分割腺瘤性结直肠息肉

摘要 结直肠癌（CRC）是影响全世界人们的主要消化道系统恶性肿瘤之一。腺瘤性息肉是 CRC 的前兆，因此，预防这些病变的发展也可以预防随后的恶性肿瘤。然而，腺瘤检出率 (ADR) 是衡量结肠镜医师识别和去除癌前结直肠息肉能力的指标，在​​内窥镜医师之间差异很大。在这里，我们尝试通过详细探索深度神经网络的多尺度性能，使用卷积神经网络 (CNN) 来生成独特的计算机辅助诊断 (CAD) 系统。我们将该系统应用于 1,197 名患者的结肠镜筛查中的 3,375 张手工标记图像；其中，3,045 个分配给训练数据集，330 个分配给测试数据集。图像被简单地诊断为腺瘤性息肉或非腺瘤性息肉。当应用于测试数据集时，我们的 CNN-CAD 系统实现了 89.5% 的平均精度。我们得出结论，尽管需要通过大型多中心试验进一步验证，但提议的框架可以增加 ADR 并降低间期 CRC 的发生率。