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Trustworthy multi-phase liver tumor segmentation via evidence-based uncertainty.

Authors :
Hu, Chuanfei
Xia, Tianyi
Cui, Ying
Zou, Quchen
Wang, Yuancheng
Xiao, Wenbo
Ju, Shenghong
Li, Xinde
Source :
Engineering Applications of Artificial Intelligence. Jul2024:Part C, Vol. 133, pN.PAG-N.PAG. 1p.
Publication Year :
2024

Abstract

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, 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 with 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. [Display omitted] • A novel trustworthy multi-phase liver tumor segmentation. • The first method for the multi-phase liver tumor segmentation toward trustworthiness. • Performance of the method is verified in terms of validity and reliability. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09521976
Volume :
133
Database :
Academic Search Index
Journal :
Engineering Applications of Artificial Intelligence
Publication Type :
Academic Journal
Accession number :
177604653
Full Text :
https://doi.org/10.1016/j.engappai.2024.108289