Liver and bile duct cancers are leading causes of worldwide cancer death. The most common ones are hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). Influencing factors and prognosis of HCC and ICC are different. Precise classification of these two liver cancers is essential for treatment and prevention plans. The aim of this study is to develop a machine-based method that differentiates between the two types of liver cancers from multi-phase abdominal computerized tomography (CT) scans. The proposed method consists of two major steps. In the first step, the liver is segmented from the original images using a convolutional neural network model, together with task-specific pre-processing and post-processing techniques. In the second step, by looking at the intensity histograms of the segmented images, we extract features from regions that are discriminating between HCC and ICC, and use them as an input for classification using support vector machine model. By testing on a dataset of labeled multi-phase CT scans provided by Maharaj Nakorn Chiang Mai Hospital, Thailand, we have obtained 88% in classification accuracy. Our proposed method has a great potential in helping radiologists diagnosing liver cancer.

中文翻译：

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基于多期 CT 扫描的肝细胞癌和肝内胆管癌的分类。

肝癌和胆管癌是全球癌症死亡的主要原因。最常见的是肝细胞癌（HCC）和肝内胆管癌（ICC）。HCC和ICC的影响因素和预后不同。这两种肝癌的精确分类对于治疗和预防计划至关重要。本研究的目的是开发一种基于机器的方法，该方法可从多相腹部计算机断层扫描 (CT) 扫描中区分两种类型的肝癌。所提出的方法包括两个主要步骤。在第一步中，使用卷积神经网络模型以及特定任务的预处理和后处理技术从原始图像中分割出肝脏。第二步，通过查看分割图像的强度直方图，我们从区分 HCC 和 ICC 的区域中提取特征，并将它们用作使用支持向量机模型进行分类的输入。通过在泰国清迈 Maharaj Nakorn 医院提供的标记多相 CT 扫描数据集上进行测试，我们获得了 88% 的分类准确率。我们提出的方法在帮助放射科医生诊断肝癌方面具有巨大潜力。