Breath-Hold CBCT-Guided CBCT-to-CT Synthesis via Multimodal Unsupervised Representation Disentanglement Learning
Author:
Affiliation:
1. School of Biomedical Engineering, Southern Medical University, Guangzhou, China
2. Department of Radiation Therapy, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China
Funder
National Natural Science Foundation of China
Basic and Applied Basic Research Foundation of Guangdong Province
Guangdong Provincial Key Laboratory of Medical Image Processing
Guangdong Provincial Hospital of Chinese Medicine Science and Technology Project
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software
Link
http://xplorestaging.ieee.org/ielx7/42/10198906/10050172.pdf?arnumber=10050172
Reference47 articles.
1. Generating synthesized computed tomography (CT) from cone-beam computed tomography (CBCT) using CycleGAN for adaptive radiation therapy
2. Diverse Image-to-Image Translation via Disentangled Representations
3. A single neural network for cone-beam computed tomography-based radiotherapy of head-and-neck, lung and breast cancer
4. Unsupervised image-to-image translation networks;liu;Proc Adv Neural Inf Process Syst,2017
5. CBCT-based synthetic CT generation using generative adversarial networks with disentangled representation
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