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Multi-phase liver contrast-enhanced computed tomography (CECT) images conveythe 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, % ofthe fused result, resulting in the implicit unreliability of clinicalapplications. In this paper, we propose a novel trustworthy multi-phase livertumor segmentation (TMPLiTS), which is a unified framework jointly conductingsegmentation and uncertainty estimation. The trustworthy results could assistthe clinicians to make a reliable diagnosis. Specifically, Dempster-ShaferEvidence Theory (DST) is introduced to parameterize the segmentation anduncertainty as evidence following Dirichlet distribution. The reliability ofsegmentation results among multi-phase CECT images is quantified explicitly.Meanwhile, a multi-expert mixture scheme (MEMS) is proposed to fuse themulti-phase evidences, which can guarantee the effect of fusion procedure basedon theoretical analysis. Experimental results demonstrate the superiority ofTMPLiTS compared with the state-of-the-art methods. Meanwhile, the robustnessof TMPLiTS is verified, where the reliable performance can be guaranteedagainst the perturbations.

Trustworthy Multi-phase Liver Tumor Segmentation via Evidence-based Uncertainty