Deep learning (DL) has become widely used for medical image segmentation in recent years. However, despite these advances, there are still problems for which DL-based segmentation fails. Recently, some DL approaches had a breakthrough by using anatom...
PURPOSE: Despite the widespread availability of in-treatment room cone beam computed tomography (CBCT) imaging, due to the lack of reliable segmentation methods, CBCT is only used for gross set up corrections in lung radiotherapies. Accurate and reli...
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
May 25, 2021
Recently, deep learning approaches have been successfully used for ultrasound (US) image artifact removal. However, paired high-quality images for supervised training are difficult to obtain in many practical situations. Inspired by the recent theory...
BACKGROUND: Early detection of laryngeal masses without periodic visits to hospitals is essential for improving the possibility of full recovery and the long-term survival ratio after prompt treatment, as well as reducing the risk of clinical infecti...
Synthetic medical image generation has a huge potential for improving healthcare through many applications, from data augmentation for training machine learning systems to preserving patient privacy. Conditional Adversarial Generative Networks (cGANs...
BACKGROUND: The image segmentation of skull CT is the cornerstone for the computer-assisted craniomaxillofacial surgery in multiple aspects. This study aims to introduce an AI-enabled automatic segmentation and propose its prospect in facilitating th...
European journal of nuclear medicine and molecular imaging
May 22, 2021
BACKGROUND: Artificial intelligence (AI) algorithms based on deep convolutional networks have demonstrated remarkable success for image transformation tasks. State-of-the-art results have been achieved by generative adversarial networks (GANs) and tr...
PURPOSE: To use deep learning for suppression of the artifact-generating T -relaxation echo in cine displacement encoding with stimulated echoes (DENSE) for the purpose of reducing the scan time.
IEEE transactions on bio-medical engineering
May 21, 2021
OBJECTIVE: The purpose of this work is to investigate the feasibility of using deep convolutional neural network (CNN) to improve the image quality of a grating-based X-ray differential phase contrast imaging (XPCI) system.
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