  Multi-phase computed tomography (CT) images provide crucial complementary
information for accurate liver tumor segmentation (LiTS). State-of-the-art
multi-phase LiTS methods usually fused cross-phase features through
phase-weighted summation or channel-attention based concatenation. However,
these methods ignored the spatial (pixel-wise) relationships between different
phases, hence leading to insufficient feature integration. In addition, the
performance of existing methods remains subject to the uncertainty in
segmentation, which is particularly acute in tumor boundary regions. In this
work, we propose a novel LiTS method to adequately aggregate multi-phase
information and refine uncertain region segmentation. To this end, we introduce
a spatial aggregation module (SAM), which encourages per-pixel interactions
between different phases, to make full use of cross-phase information.
Moreover, we devise an uncertain region inpainting module (URIM) to refine
uncertain pixels using neighboring discriminative features. Experiments on an
in-house multi-phase CT dataset of focal liver lesions (MPCT-FLLs) demonstrate
that our method achieves promising liver tumor segmentation and outperforms
state-of-the-arts.
