This paper presents a novel, fully automatic approach based on a fully convolutional network (FCN) for segmenting liver tumors from CT images. Specifically, we designed a multi-channel fully convolutional network (MC-FCN) to segment liver tumors from multiphase contrast-enhanced CT images. Because each
phase of contrast-enhanced data provides distinct information on pathological features, we trained one network for each phase of the CT images and fused their high-layer features together. The proposed approach was validated on CT images taken from two databases: 3Dircadb and JDRD. In the case of 3Dircadb, using the FCN, the mean ratios of the volumetric overlap error (VOE), relative volume difference (RVD), average symmetric surface distance (ASD), root mean square symmetric surface distance (RMSD) and maximum symmetric surface distance (MSSD) were 15.6 ± 4.3%, 5.8 ± 3.5%, 2.0 ± 0.9%, 2.9 ± 1.5 mm,
7.1 ± 6.2 mm, respectively. For JDRD, using the MC-FCN, the mean ratios of VOE, RVD, ASD, RMSD, and MSSD were 8.1 ± 4.5%, 1.7 ± 1.0%, 1.5 ± 0.7%, 2.0 ± 1.2 mm, 5.2 ± 6.4 mm, respectively. The test results demonstrate that the MC-FCN model provides greater accuracy and robustness than previous methods.
本文提出了一种基于完全卷积网络（FCN）的新型全自动方法，用于从CT图像中分割肝脏肿瘤。具体来说，我们设计了一个多通道完全卷积网络（MC-FCN）来分割来自多相增强CT图像的肝肿瘤。因为每个
对比增强数据的相位提供了关于病理特征的独特信息，我们为CT图像的每个阶段训练了一个网络并将它们的高层特征融合在一起。所提出的方法在从两个数据库获取的CT图像上验证：3Dircadb和JDRD。在3Dircadb的情况下，使用FCN，体积重叠误差（VOE），相对体积差（RVD），平均对称表面距离（ASD），均方根对称表面距离（RMSD）和最大对称表面距离距离（MSSD）分别为15.6±4.3％，5.8±3.5％，2.0±0.9％，2.9±1.5mm，