  In this paper, we propose a phase attention residual network (PA-ResSeg) to
model multi-phase features for accurate liver tumor segmentation, in which a
phase attention (PA) is newly proposed to additionally exploit the images of
arterial (ART) phase to facilitate the segmentation of portal venous (PV)
phase. The PA block consists of an intra-phase attention (Intra-PA) module and
an inter-phase attention (Inter-PA) module to capture channel-wise
self-dependencies and cross-phase interdependencies, respectively. Thus it
enables the network to learn more representative multi-phase features by
refining the PV features according to the channel dependencies and
recalibrating the ART features based on the learned interdependencies between
phases. We propose a PA-based multi-scale fusion (MSF) architecture to embed
the PA blocks in the network at multiple levels along the encoding path to fuse
multi-scale features from multi-phase images. Moreover, a 3D boundary-enhanced
loss (BE-loss) is proposed for training to make the network more sensitive to
boundaries. To evaluate the performance of our proposed PA-ResSeg, we conducted
experiments on a multi-phase CT dataset of focal liver lesions (MPCT-FLLs).
Experimental results show the effectiveness of the proposed method by achieving
a dice per case (DPC) of 0.77.87, a dice global (DG) of 0.8682, a volumetric
overlap error (VOE) of 0.3328 and a relative volume difference (RVD) of 0.0443
on the MPCT-FLLs. Furthermore, to validate the effectiveness and robustness of
PA-ResSeg, we conducted extra experiments on another multi-phase liver tumor
dataset and obtained a DPC of 0.8290, a DG of 0.9132, a VOE of 0.2637 and a RVD
of 0.0163. The proposed method shows its robustness and generalization
capability in different datasets and different backbones.
