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.

翻译：多期肝脏对比增强计算机断层扫描（CECT）图像提供了用于肝脏肿瘤分割（LiTS）的互补多期信息，这对于临床辅助肝癌诊断至关重要。然而，现有的基于多期肝脏肿瘤分割（MPLiTS）的方法存在冗余性和弱可解释性问题，导致融合结果的临床应用中隐含不可靠性。本文提出了一种新颖的可信赖多期肝脏肿瘤分割方法（TMPLiTS），它是一个统一框架，同时进行分割和不确定性估计。可信赖的结果可辅助临床医生做出可靠诊断。具体而言，引入Dempster-Shafer证据理论（DST）将分割和不确定性参数化为服从Dirichlet分布的证据，显式量化了多期CECT图像中分割结果的可靠性。同时，提出了一种多专家混合方案（MEMS）来融合多期证据，基于理论分析保证了融合过程的有效性。实验结果表明，TMPLiTS相比现有最先进方法具有优越性。同时，验证了TMPLiTS的鲁棒性，在扰动情况下仍可保证可靠性能。