用于对比增强 CT 中肝脏肿瘤分割和微血管侵犯评估的多相位特征交互变压器网络

IF 2.6 4区 工程技术 Q1 Mathematics
Wencong Zhang, Yuxi Tao, Zhanyao Huang, Yue Li, Yingjia Chen, Tengfei Song, Xiangyuan Ma, Yaqin Zhang
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引用次数: 0

摘要

从计算机断层扫描(CT)扫描中精确分割肝脏肿瘤是各种临床应用的前提步骤。多相 CT 成像可增强肿瘤特征描述,从而帮助放射科医生准确识别肿瘤。然而,现有的肝脏肿瘤自动分割模型并未充分利用多相信息,也缺乏捕捉全局信息的能力。在这项研究中,我们开发了一种开创性的多相位特征交互变换器网络(MI-TransSeg),用于在对比增强 CT 图像中准确地进行肝脏肿瘤分割和随后的微血管侵犯(MVI)评估。在所提出的网络中,引入了一个高效的多相位特征交互模块,以实现多相位之间的双向特征交互,从而最大限度地利用可用的多相位信息。为了增强模型提取全局信息的能力,我们设计了一种基于分层变压器的编码器和解码器架构。重要的是,我们设计了一种多分辨率尺度特征聚合策略(MSFA),以优化所提模型的参数和性能。分割后,MI-TransSeg 生成的肝脏肿瘤掩膜被用于提取放射学特征,以用于 MVI 评估的临床应用。经机构审查委员会(IRB)批准,收集了临床多相对比增强腹部 CT 数据集,其中包括 164 名肝脏肿瘤患者。实验结果表明,所提出的 MI-TransSeg 优于各种最先进的方法。此外,我们还发现,我们的方法所预测的肿瘤掩膜在评估微血管侵犯方面表现出了良好的潜力。总之,MI-TransSeg 为复杂肝脏肿瘤的分割提供了一个创新范例,从而强调了多相 CT 数据利用的重要性。建议的 MI-TransSeg 网络有可能帮助放射科医生诊断肝脏肿瘤和评估微血管侵犯。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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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来源期刊
Mathematical Biosciences and Engineering
Mathematical Biosciences and Engineering 工程技术-数学跨学科应用
CiteScore
3.90
自引率
7.70%
发文量
586
审稿时长
>12 weeks
期刊介绍: Mathematical Biosciences and Engineering (MBE) is an interdisciplinary Open Access journal promoting cutting-edge research, technology transfer and knowledge translation about complex data and information processing. MBE publishes Research articles (long and original research); Communications (short and novel research); Expository papers; Technology Transfer and Knowledge Translation reports (description of new technologies and products); Announcements and Industrial Progress and News (announcements and even advertisement, including major conferences).
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