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.
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中的先决条件。多期CT成像增强肿瘤定性，从而帮助放射科医生准确识别。然而,现有的自动肝肿瘤分割模型没有充分利用多阶段信息，缺乏捕获全局信息的能力。在这项研究中,我们开发了一种开创性的多相特征交互变压器网络（MI-TransSeg），用于在对比增强CT图像中进行准确的肝肿瘤分割和随后的微血管侵犯（MVI）评估。在拟议的网络中，引入了高效的多阶段特征交互模块，实现了多阶段之间的双向特征交互，从而最大限度地利用可用的多相信息。为了增强模型提取全局信息的能力，设计了一种基于层次变换器的编码器和解码器体系结构。重要的是,我们设计了一种多分辨率尺度特征聚合策略（MSFA）来优化所提出模型的参数和性能。在分割之后，通过MI-TransSeg生成的肝肿瘤面罩用于提取影像组学特征,以用于MVI评估的临床应用.经机构审查委员会(IRB)批准，我们收集了临床多期对比增强CT腹部数据集,其中包括164例肝肿瘤患者.实验结果表明，所提出的MI-TransSeg优于各种最先进的方法。此外,我们发现我们的方法预测的肿瘤面罩在评估微血管侵犯方面显示出有希望的潜力.总之,MI-TransSeg为复杂肝肿瘤的分割提供了创新的范例，从而强调了多期CT数据开发的重要性。所提出的MI-TransSeg网络具有协助放射科医师诊断肝肿瘤和评估微血管侵犯的潜力。
