  Multi-phase liver contrast-enhanced computed tomography (CECT) images convey
the complementary multi-phase information for liver tumor segmentation (LiTS),
which are crucial to assist the diagnosis of liver cancer clinically. However,
the performances of existing multi-phase liver tumor segmentation
(MPLiTS)-based methods suffer from redundancy and weak interpretability, % of
the fused result, resulting in the implicit unreliability of clinical
applications. In this paper, we propose a novel trustworthy multi-phase liver
tumor segmentation (TMPLiTS), which is a unified framework jointly conducting
segmentation and uncertainty estimation. The trustworthy results could assist
the clinicians to make a reliable diagnosis. Specifically, Dempster-Shafer
Evidence Theory (DST) is introduced to parameterize the segmentation and
uncertainty as evidence following Dirichlet distribution. The reliability of
segmentation results among multi-phase CECT images is quantified explicitly.
Meanwhile, a multi-expert mixture scheme (MEMS) is proposed to fuse the
multi-phase evidences, which can guarantee the effect of fusion procedure based
on theoretical analysis. Experimental results demonstrate the superiority of
TMPLiTS compared with the state-of-the-art methods. Meanwhile, the robustness
of TMPLiTS is verified, where the reliable performance can be guaranteed
against the perturbations.
