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

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 mo...

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Vydáno v:Mathematical biosciences and engineering : MBE Ročník 21; číslo 4; s. 5735 - 5761
Hlavní autoři: Zhang, Wencong, Tao, Yuxi, Huang, Zhanyao, Li, Yue, Chen, Yingjia, Song, Tengfei, Ma, Xiangyuan, Zhang, Yaqin
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States AIMS Press 24.04.2024
Témata:
Algorithms
Contrast Media
contrast-enhanced ct
Female
Humans
Image Processing, Computer-Assisted - methods
Liver - blood supply
Liver - diagnostic imaging
Liver - pathology
Liver Neoplasms - blood supply
Liver Neoplasms - diagnostic imaging
Liver Neoplasms - pathology
liver tumor segmentation
Male
Microvessels - diagnostic imaging
Microvessels - pathology
multi-phase
Neoplasm Invasiveness
Radiographic Image Interpretation, Computer-Assisted - methods
Tomography, X-Ray Computed
transformer
liver tumor segmentation
transformer
contrast-enhanced CT
multi-phase
ISSN:1551-0018, 1551-0018
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Shrnutí: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.
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ISSN:1551-0018
1551-0018
DOI:10.3934/mbe.2024253