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Multi-phase features interaction transformer network for liver tumor segmentation and microvascular invasion assessment in contrast-enhanced CT

Authors :
Wencong Zhang
Yuxi Tao
Zhanyao Huang
Yue Li
Yingjia Chen
Tengfei Song
Xiangyuan Ma
Yaqin Zhang
Source :
Mathematical Biosciences and Engineering, Vol 21, Iss 4, Pp 5735-5761 (2024)
Publication Year :
2024
Publisher :
AIMS Press, 2024.

Abstract

Precise segmentation of liver tumors from computed tomography (CT) scans is a prerequisite step in various clinical applications. Multi-phase CT imaging enhances tumor characterization, thereby assisting radiologists in accurate identification. However, existing automatic liver tumor segmentation models did not fully exploit multi-phase information and lacked the capability to capture global information. In this study, we developed a pioneering multi-phase feature interaction Transformer network (MI-TransSeg) for accurate liver tumor segmentation and a subsequent microvascular invasion (MVI) assessment in contrast-enhanced CT images. In the proposed network, an efficient multi-phase features interaction module was introduced to enable bi-directional feature interaction among multiple phases, thus maximally exploiting the available multi-phase information. To enhance the model's capability to extract global information, a hierarchical transformer-based encoder and decoder architecture was designed. Importantly, we devised a multi-resolution scales feature aggregation strategy (MSFA) to optimize the parameters and performance of the proposed model. Subsequent to segmentation, the liver tumor masks generated by MI-TransSeg were applied to extract radiomic features for the clinical applications of the MVI assessment. With Institutional Review Board (IRB) approval, a clinical multi-phase contrast-enhanced CT abdominal dataset was collected that included 164 patients with liver tumors. The experimental results demonstrated that the proposed MI-TransSeg was superior to various state-of-the-art methods. Additionally, we found that the tumor mask predicted by our method showed promising potential in the assessment of microvascular invasion. In conclusion, MI-TransSeg presents an innovative paradigm for the segmentation of complex liver tumors, thus underscoring the significance of multi-phase CT data exploitation. The proposed MI-TransSeg network has the potential to assist radiologists in diagnosing liver tumors and assessing microvascular invasion.

Details

Language :
English
ISSN :
15510018
Volume :
21
Issue :
4
Database :
Directory of Open Access Journals
Journal :
Mathematical Biosciences and Engineering
Publication Type :
Academic Journal
Accession number :
edsdoj.8eab0ac54ab347e884eef501e298049c
Document Type :
article
Full Text :
https://doi.org/10.3934/mbe.2024253?viewType=HTML