A review on medical imaging synthesis using deep learning and its clinical applications
This paper reviewed the deep learning‐based studies for medical imaging synthesis and its
clinical application. Specifically, we summarized the recent developments of deep learning …
clinical application. Specifically, we summarized the recent developments of deep learning …
A review of deep learning based methods for medical image multi-organ segmentation
Deep learning has revolutionized image processing and achieved the-state-of-art
performance in many medical image segmentation tasks. Many deep learning-based …
performance in many medical image segmentation tasks. Many deep learning-based …
A survey on deep learning in medicine: Why, how and when?
New technologies are transforming medicine, and this revolution starts with data. Health
data, clinical images, genome sequences, data on prescribed therapies and results …
data, clinical images, genome sequences, data on prescribed therapies and results …
2D medical image synthesis using transformer-based denoising diffusion probabilistic model
Objective. Artificial intelligence (AI) methods have gained popularity in medical imaging
research. The size and scope of the training image datasets needed for successful AI model …
research. The size and scope of the training image datasets needed for successful AI model …
[HTML][HTML] Clinical implementation of artificial intelligence-driven cone-beam computed tomography-guided online adaptive radiotherapy in the pelvic region
Background and purpose Studies have demonstrated the potential of online adaptive
radiotherapy (oART). However, routine use has been limited due to resource demanding …
radiotherapy (oART). However, routine use has been limited due to resource demanding …
CBCT‐based synthetic CT generation using deep‐attention cycleGAN for pancreatic adaptive radiotherapy
Purpose Current clinical application of cone‐beam CT (CBCT) is limited to patient setup.
Imaging artifacts and Hounsfield unit (HU) inaccuracy make the process of CBCT‐based …
Imaging artifacts and Hounsfield unit (HU) inaccuracy make the process of CBCT‐based …
Improving CBCT quality to CT level using deep learning with generative adversarial network
Purpose To improve image quality and computed tomography (CT) number accuracy of
daily cone beam CT (CBCT) through a deep learning methodology with generative …
daily cone beam CT (CBCT) through a deep learning methodology with generative …
Machine learning in quantitative PET: A review of attenuation correction and low-count image reconstruction methods
The rapid expansion of machine learning is offering a new wave of opportunities for nuclear
medicine. This paper reviews applications of machine learning for the study of attenuation …
medicine. This paper reviews applications of machine learning for the study of attenuation …
Artificial intelligence in radiotherapy
G Li, X Wu, X Ma - Seminars in Cancer Biology, 2022 - Elsevier
Radiotherapy is a discipline closely integrated with computer science. Artificial intelligence
(AI) has developed rapidly over the past few years. With the explosive growth of medical big …
(AI) has developed rapidly over the past few years. With the explosive growth of medical big …
Male pelvic multi-organ segmentation using token-based transformer Vnet
Objective. This work aims to develop an automated segmentation method for the prostate
and its surrounding organs-at-risk in pelvic computed tomography to facilitate prostate …
and its surrounding organs-at-risk in pelvic computed tomography to facilitate prostate …