基于深度学习的CT图像分割技术在自动勾画中的研究进展与应用

Advances and Applications of Deep Learning-Based CT Image Segmentation Techniques for Automatic Delineation

摘要: 计算机断层扫描(CT)在现代临床诊疗中发挥着不可替代的作用，尤其在肿瘤靶区勾画、术前评估和放疗规划等环节中对图像标注的精准性提出了更高要求。然而，传统人工勾画方式存在主观性强、效率低、重复性差等问题，制约了其标准化推广。近年来，人工智能技术迅速发展，特别是基于深度学习的图像分割模型(如U-Net、3D U-Net、TransUNet等)在CT影像结构识别中展现出卓越性能。本文系统梳理了AI辅助CT勾画的关键技术路径与模型演化趋势，分析了其在肝脏、肺部、脑部等典型病种中的应用实践，探讨了系统部署中面临的可解释性、数据安全及泛化能力问题，并展望了联邦学习、主动学习、大模型融合等新兴技术对临床智能化发展的推动作用。旨在为CT影像勾画的标准化、智能化与多中心应用提供理论依据与实践参考。

Abstract: Computed tomography (CT) plays a pivotal role in modern clinical diagnosis and treatment planning, especially in tasks such as tumor target delineation, preoperative evaluation, and radiotherapy design. However, conventional manual contouring remains labor-intensive, time-consuming, and subject to significant inter-operator variability, limiting its scalability and standardization. Recent advances in artificial intelligence (AI), particularly deep learning-based segmentation models such as U-Net, 3D U-Net, and TransUNet, have demonstrated promising performance in automatic structure identification on CT images. This review provides a comprehensive overview of the core methodologies and technical evolution underlying AI-assisted CT delineation. We analyze representative applications in liver, lung, and brain disease scenarios, highlighting model design strategies, data preparation pipelines, and integration with clinical workflows. Challenges associated with real-world deployment—such as model interpretability, domain generalization, and data privacy—are critically discussed. Furthermore, we explore the potential of federated learning, self-supervised learning, and multimodal foundation models to enhance robustness, scalability, and cross-institutional adaptation. By synthesizing technological advances with clinical demands, this review aims to support the development of intelligent, standardized, and privacy-preserving CT annotation systems that can be reliably deployed in multicenter clinical environments.