Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer in adults, and the most common cause of death of people suffering from cirrhosis. The segmentation of liver lesions in CT images allows assessment of tumor load, treatment planning, prognosis and monitoring of treatment response. Manual segmentation is a very time-consuming task and in many cases, prone to inaccuracies and automatic tools for tumor detection and segmentation are desirable. In this paper, we use a network architecture that consists of two consecutive fully convolutional neural networks. The first network segments the liver whereas the second network segments the actual tumor inside the liver. Our network is trained on a subset of the LiTS (Liver Tumor Segmentation) challenge and evaluated on data provided from the radiological center in Innsbruck.
翻译:肝细胞癌(HCC)是成人最常见的主要肝癌类型,也是肝硬化患者死亡的最常见原因。CT图像中的肝脏损伤分解可以评估肿瘤负荷、治疗规划、预测和监测治疗反应。人工分解是一项非常耗时的工作,在许多情况下,容易出现肿瘤检测和分解的不准确和自动工具。在本文中,我们使用的网络结构由两个连续的完全共生神经网络组成。第一个网络部分是肝脏,第二个网络部分是肝脏内的实际肿瘤。我们的网络接受LITS(Liver Temor分解)挑战的一组培训,并评价了因斯布鲁克辐射中心提供的数据。