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| Deep Learning Methods for Image Analysis and Synthesis for Intensity Modulated Radiotherapy:a Review |
| Liu Guocai1,2#*, Gu Dongdong1, Liu Xiao1, Liu Jinguang1, Liu Yanfei1, Zhang Maodan1 |
1(College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)
2(National EngineeringResearch Center for Robot Visual Perception and Control Technology, Changsha 410082, China) |
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Abstract Cancer is a common problem that seriously threatens human health. 60% to 70% of cancer patients need radiotherapy. Currently intensity modulated radiotherapy (IMRT) is one main radiotherapy technique that is widely applied in clinics. This paper reviewed deep learning methods, key technologies and future directions for IMRT, including clinical CT/CBCT/MRI/PET-guided IMRT technologies, and supervised or unsupervised deep convolutional neural networks or generative adversarial networks for the segmentation, registration and image-to-image translation of CT/CBCT/MRI/PET images of tumors.
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Received: 16 November 2020
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Corresponding Authors:
*E-mail: lgc630819@hnu.edu.cn
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| About author:: #Member, Chinese Society of Biomedical Engineering |
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