Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
Institute for Artificial Intelligence in Medicine, University Hospital Essen, Girardetstrasse 2, D-45131 Essen, Germany.
Phys Med Biol. 2022 Aug 18;67(17). doi: 10.1088/1361-6560/ac840f.
Head and neck surgery is a fine surgical procedure with a complex anatomical space, difficult operation and high risk. Medical image computing (MIC) that enables accurate and reliable preoperative planning is often needed to reduce the operational difficulty of surgery and to improve patient survival. At present, artificial intelligence, especially deep learning, has become an intense focus of research in MIC. In this study, the application of deep learning-based MIC in head and neck surgery is reviewed. Relevant literature was retrieved on the Web of Science database from January 2015 to May 2022, and some papers were selected for review from mainstream journals and conferences, such as IEEE Transactions on Medical Imaging, Medical Image Analysis, Physics in Medicine and Biology, Medical Physics, MICCAI, etc. Among them, 65 references are on automatic segmentation, 15 references on automatic landmark detection, and eight references on automatic registration. In the elaboration of the review, first, an overview of deep learning in MIC is presented. Then, the application of deep learning methods is systematically summarized according to the clinical needs, and generalized into segmentation, landmark detection and registration of head and neck medical images. In segmentation, it is mainly focused on the automatic segmentation of high-risk organs, head and neck tumors, skull structure and teeth, including the analysis of their advantages, differences and shortcomings. In landmark detection, the focus is mainly on the introduction of landmark detection in cephalometric and craniomaxillofacial images, and the analysis of their advantages and disadvantages. In registration, deep learning networks for multimodal image registration of the head and neck are presented. Finally, their shortcomings and future development directions are systematically discussed. The study aims to serve as a reference and guidance for researchers, engineers or doctors engaged in medical image analysis of head and neck surgery.
头颈部外科是一种精细的外科手术，具有复杂的解剖空间、操作难度大且风险高的特点。为了降低手术操作难度，提高患者生存率，通常需要医学影像计算（MIC）来实现准确可靠的术前规划。目前，人工智能，尤其是深度学习，已成为 MIC 研究的热点。本文综述了基于深度学习的 MIC 在头颈部外科中的应用。在 Web of Science 数据库中检索了 2015 年 1 月至 2022 年 5 月的相关文献，并从 IEEE Transactions on Medical Imaging、Medical Image Analysis、Physics in Medicine and Biology、Medical Physics、MICCAI 等主流期刊和会议中选择了一些论文进行综述。其中，65 篇论文涉及自动分割，15 篇论文涉及自动标记检测，8 篇论文涉及自动配准。在综述中，首先介绍了 MIC 中的深度学习。然后，根据临床需求，系统总结了深度学习方法的应用，并将其概括为头颈部医学图像的分割、标记检测和配准。在分割方面，主要侧重于高风险器官、头颈部肿瘤、颅骨结构和牙齿的自动分割，包括对其优缺点的分析。在标记检测方面，主要介绍了头颈部侧位和颅颌面图像的标记检测，分析了其优缺点。在配准方面，介绍了头颈部多模态图像配准的深度学习网络。最后，系统地讨论了它们的优缺点和未来的发展方向。本文旨在为从事头颈部外科医学图像分析的研究人员、工程师或医生提供参考和指导。
