Deep learning in medical image registration: a review

Y Fu, Y Lei, T Wang, WJ Curran, T Liu… - Physics in Medicine & …, 2020‏ - iopscience.iop.org
This paper presents a review of deep learning (DL)-based medical image registration
methods. We summarized the latest developments and applications of DL-based registration …

A review of deep learning based methods for medical image multi-organ segmentation

Y Fu, Y Lei, T Wang, WJ Curran, T Liu, X Yang - Physica Medica, 2021‏ - Elsevier
Deep learning has revolutionized image processing and achieved the-state-of-art
performance in many medical image segmentation tasks. Many deep learning-based …

A review on medical imaging synthesis using deep learning and its clinical applications

T Wang, Y Lei, Y Fu, JF Wynne… - Journal of applied …, 2021‏ - Wiley Online Library
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 …

[HTML][HTML] A review of synthetic image data and its use in computer vision

K Man, J Chahl - Journal of Imaging, 2022‏ - mdpi.com
Development of computer vision algorithms using convolutional neural networks and deep
learning has necessitated ever greater amounts of annotated and labelled data to produce …

Deep learning based synthetic‐CT generation in radiotherapy and PET: a review

MF Spadea, M Maspero, P Zaffino, J Seco - Medical physics, 2021‏ - Wiley Online Library
Abstract Recently, deep learning (DL)‐based methods for the generation of synthetic
computed tomography (sCT) have received significant research attention as an alternative to …

CBCT‐Based synthetic CT image generation using conditional denoising diffusion probabilistic model

J Peng, RLJ Qiu, JF Wynne, CW Chang, S Pan… - Medical …, 2024‏ - Wiley Online Library
Background Daily or weekly cone‐beam computed tomography (CBCT) scans are
commonly used for accurate patient positioning during the image‐guided radiotherapy …

CBCT‐based synthetic CT generation using deep‐attention cycleGAN for pancreatic adaptive radiotherapy

Y Liu, Y Lei, T Wang, Y Fu, X Tang, WJ Curran… - Medical …, 2020‏ - Wiley Online Library
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 …

Supervised learning with cyclegan for low-dose FDG PET image denoising

L Zhou, JD Schaefferkoetter, IWK Tham, G Huang… - Medical image …, 2020‏ - Elsevier
PET imaging involves radiotracer injections, raising concerns about the risk of radiation
exposure. To minimize the potential risk, one way is to reduce the injected tracer. However …

Updated primer on generative artificial intelligence and large language models in medical imaging for medical professionals

K Kim, K Cho, R Jang, S Kyung, S Lee… - Korean Journal of …, 2024‏ - pmc.ncbi.nlm.nih.gov
The emergence of Chat Generative Pre-trained Transformer (ChatGPT), a chatbot
developed by OpenAI, has garnered interest in the application of generative artificial …

Creating artificial images for radiology applications using generative adversarial networks (GANs)–a systematic review

V Sorin, Y Barash, E Konen, E Klang - Academic radiology, 2020‏ - Elsevier
Rationale and Objectives Generative adversarial networks (GANs) are deep learning
models aimed at generating fake realistic looking images. These novel models made a great …