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 …

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 …

Deep learning has revolutionized image processing and achieved the-state-of-art
performance in many medical image segmentation tasks. Many deep learning-based …

Purpose In radiotherapy, MRI is used for target volume and organs-at-risk delineation for its
superior soft-tissue contrast as compared to CT imaging. However, MRI does not provide the …

Purpose To improve image quality and computed tomography (CT) number accuracy of
daily cone beam CT (CBCT) through a deep learning methodology with generative …

Over the last years, technological innovation in Radiotherapy (RT) led to the introduction of
Magnetic Resonance-guided RT (MRgRT) systems. Due to the higher soft tissue contrast …

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 …

Deep Learning approaches for automatic segmentation of organs from CT scans and MRI
are providing promising results, leading towards a revolution in the radiologists' workflow …

This paper surveys the data‐driven dose prediction methods investigated for knowledge‐
based planning (KBP) in the last decade. These methods were classified into two major …

Hepatocellular carcinoma (HCC) accounts for 90% of liver tumours and is one of the leading
causes of mortality. Cirrhosis due to viral hepatitis, alcohol or steatohepatitis is the major risk …