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：多阶段肝脏增强计算机断层扫描（CECT）图像承载了肝脏肿瘤分割（LiTS）所需的互补性多阶段信息，这对临床辅助诊断肝癌至关重要。然而，现有基于多阶段肝脏肿瘤分割（MPLiTS）方法存在融合结果冗余且可解释性弱的问题，导致临床应用中隐含不可靠性。本文提出一种新型可信多阶段肝脏肿瘤分割方法（TMPLiTS），该统一框架可同时完成分割与不确定性估计，其可信结果为临床医生提供可靠诊断依据。具体而言，引入Dempster-Shafer证据理论（DST）将分割结果与不确定性参数化为服从狄利克雷分布的证据，明确量化多阶段CECT图像中分割结果的可靠性。同时，提出多专家混合方案（MEMS）融合多阶段证据，通过理论分析保证融合过程的可靠性。实验结果表明，TMPLiTS相较于现有最优方法具有优越性，且其鲁棒性得以验证，即使在扰动条件下仍能保持可靠的性能表现。