Abstract―This paper presented an approach used Fully Convolutional Networks (FCN) to segment liver tumor in
Computed Tomography (CT) images. In addition, using different characteristics of scan quality and tumor conspicuity among portal venous phase, arterial phase and equilibrium phase, we proposed an automatic liver tumor segmentation with Multiple Kernel Fully Convolutional Networks (MK-FCN). MK-FCN can segment liver tumor from multi-phase contrast-enhanced CT images by using different characteristics of scan quality and tumor conspicuity among different phases. Experiments proved the effectiveness of this method in the liver tumor segmentation.
Computed Tomography (CT) images. In addition, using different characteristics of scan quality and tumor conspicuity among portal venous phase, arterial phase and equilibrium phase, we proposed an automatic liver tumor segmentation with Multiple Kernel Fully Convolutional Networks (MK-FCN). MK-FCN can segment liver tumor from multi-phase contrast-enhanced CT images by using different characteristics of scan quality and tumor conspicuity among different phases. Experiments proved the effectiveness of this method in the liver tumor segmentation.
Keywords―Multiple Kernel; liver tumor segmentation; multi-phase contrast-enhanced
摘要 - 本文提出了一种使用完全卷积网络（FCN）来分割肝脏肿瘤的方法计算机断层扫描（CT）图像。 此外，在门静脉期，动脉期和平衡期，利用扫描质量和肿瘤显着性的不同特点，提出了一种多核全卷积网络（MK-FCN）的肝肿瘤自动分割方法。 MK-FCN可以通过不同阶段扫描质量和肿瘤显着性的不同特点，从多相增强CT图像中分割出肝肿瘤。 实验证明了该方法在肝肿瘤分割中的有效性。
关键词 - 多核; 肝肿瘤分割; 多相对比增强