1. a. Department of Medical Equipment|b. Department of Medical Imaging, Nanjing First Hospital, Nanjing Medical University,
Nanjing Jiangsu 210006, China|2. Nanjing Emergency Medical Center, Nanjing Jiangsu 210003, China
Abstract：The delineation of target volumes and organs at risk in radiotherapy images is a key step in the formulation of radiotherapy plan, they are often sketched by the radiotherapy physician manually in clinical at present. Image segmentation technology can divide the area with similar attributes in the image, which is one of the main techniques in image processing of tumor radiotherapy.Traditional image segmentation methods include edge-based, region-based growth, energy-minimization and so on, each method has
its own advantages. With the demand of medical services and the application of artificial intelligence, the auto-segmentation methods based on atlas database and deep learning have emerged one after another, especially the application of various deep learning models, which not only improves the efficiency of treatment, but also has important significance in promoting the progress and development of radiotherapy technology.
吴倩倩1a，周蕾蕾1b，赵紫婷1a，蒋红兵1a,2. 图像分割在肿瘤放射治疗中的发展与应用[J]. 中国医疗设备, 2020, 35(12): 33-36.
WU Qianqian1a，ZHOU Leilei1b，ZHAO Ziting1a，JIANG Hongbing1a,2. Development and Application of Image Segmentation in Tumor Radiotherapy. China Medical Devices, 2020, 35(12): 33-36.
