The radiotherapy (RT) process from planning to treatment delivery is a multistep, complex
operation involving numerous levels of human-machine interaction and requiring high …

Accurate liver tumor segmentation without contrast agents (non-enhanced images) avoids
the contrast-agent-associated time-consuming and high risk, which offers radiologists quick …

Early diagnosis of Alzheimer's disease (AD) is a very challenging problem and has been
attempted through data-driven methods in recent years. However, considering the inherent …

Whole-brain radiotherapy (WBRT) plays an irreplaceable role in the treatment of brain
metastases (BMs), but cognitive decline after WBRT seriously affects patients' quality of life …

Purpose. To develop and evaluate the performance of a deep learning model to generate
synthetic pulmonary perfusion images from clinical 4DCT images for patients undergoing …

Precise segmentation of the hippocampus is essential for various human brain activity and
neurological disorder studies. To overcome the small size of the hippocampus and the low …

Background and purpose Hippocampal avoidance whole brain radiotherapy (HA-WBRT) is
effective for controlling disease and preserving neuro-cognitive function for brain …

Purpose To validate a CT-based deep learning (DL) hippocampal segmentation model
trained on a single-institutional dataset and explore its utility for multi-institutional contour …

Magnetic resonance imaging (MRI) is a valuable clinical tool for displaying anatomical
structures and aiding in accurate diagnosis. Medical image super-resolution (SR) …

Hippocampal-sparing brain radiotherapy (HS-BRT) in cancer patients results in preservation
of neurocognitive function after brain RT which can contribute to patients' quality of life …