深度学习
计算机科学
跟踪(教育)
人工智能
放射治疗
模式
叙述性评论
计算机视觉
医学物理学
医学
放射科
心理学
教育学
重症监护医学
社会科学
社会学
作者
Liu Xi,Li-Sheng Geng,David Huang,Jing Cai,Ruijie Yang
出处
期刊:Quantitative imaging in medicine and surgery
[AME Publishing Company]
日期:2024-03-01
卷期号:14 (3): 2671-2692
标识
DOI:10.21037/qims-23-1489
摘要
Background and Objective: As one of the main treatment modalities, radiotherapy (RT) (also known as radiation therapy) plays an increasingly important role in the treatment of cancer. RT could benefit greatly from the accurate localization of the gross tumor volume and circumambient organs at risk (OARs). Modern linear accelerators (LINACs) are typically equipped with either gantry-mounted or room-mounted X-ray imaging systems, which provide possibilities for marker-less tracking with two-dimensional (2D) kV X-ray images. However, due to organ overlapping and poor soft tissue contrast, it is challenging to track the target directly and precisely with 2D kV X-ray images. With the flourishing development of deep learning in the field of image processing, it is possible to achieve real-time marker-less tracking of targets with 2D kV X-ray images in RT using advanced deep-learning frameworks. This article sought to review the current development of deep learning-based target tracking with 2D kV X-ray images and discuss the existing limitations and potential solutions. Finally, it also discusses some common challenges and potential future developments.
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