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

Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting