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Multi-phase features interaction transformer network for liver tumor segmentation and microvascular invasion assessment in contrast-enhanced CT.
Zhang, Wencong; Tao, Yuxi; Huang, Zhanyao; Li, Yue; Chen, Yingjia; Song, Tengfei; Ma, Xiangyuan; Zhang, Yaqin.
Afiliação
  • Zhang W; Department of Biomedical Engineering, College of Engineering, Shantou University, Shantou, China.
  • Tao Y; Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore.
  • Huang Z; Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China.
  • Li Y; Department of Biomedical Engineering, College of Engineering, Shantou University, Shantou, China.
  • Chen Y; School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.
  • Song T; Department of Biomedical Engineering, College of Engineering, Shantou University, Shantou, China.
  • Ma X; Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China.
  • Zhang Y; Department of Biomedical Engineering, College of Engineering, Shantou University, Shantou, China.
Math Biosci Eng ; 21(4): 5735-5761, 2024 Apr 24.
Article em En | MEDLINE | ID: mdl-38872556
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.
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Texto completo: 1 Temas: ECOS / Aspectos_gerais Bases de dados: MEDLINE Assunto principal: Algoritmos / Tomografia Computadorizada por Raios X / Meios de Contraste / Microvasos / Neoplasias Hepáticas Limite: Female / Humans / Male Idioma: En Revista: Math Biosci Eng Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Temas: ECOS / Aspectos_gerais Bases de dados: MEDLINE Assunto principal: Algoritmos / Tomografia Computadorizada por Raios X / Meios de Contraste / Microvasos / Neoplasias Hepáticas Limite: Female / Humans / Male Idioma: En Revista: Math Biosci Eng Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China