Automatic liver tumor segmentation can facilitate the planning of liver interventions. For
diagnosis of hepatocellular carcinoma, dynamic contrast-enhanced MRI (DCE-MRI) can …

Objectives To develop and validate a deep learning–based algorithm for segmenting and
quantifying the physiological and diseased aorta in computed tomography angiographies …

When finger joints become immobile due to an accident during sports or a widespread
disease such as rheumatoid arthritis, customised finger joint implants are to be created. In …

The quality and acceptance of machine learning (ML) approaches in cardiovascular data
interpretation depends strongly on model design and training and the interaction with the …

Ziel Die DRG-ÖRG-IRP (Deutsche Röntgengesellschaft-Österreichische
Röntgengesellschaft Internationale Radiomics-Plattform) stellt eine web-/cloudbasierte …

Goal: We introduce an in-vivo validated finite element (FE) simulation approach for
predicting individual knee joint kinematics. Our vision is to improve clinicians' understanding …

Inflammatory white matter brain lesions are a key pathological finding in patients suffering
from multiple sclerosis (MS). Image based quantification of different characteristics of these …

Purpose Computed tomography (CT) is widely used to identify anomalies in brain tissues
because their localization is important for diagnosis and therapy planning. Due to the …

Stereotactic neurosurgery requires a careful planning of cannulae paths to spare eloquent
areas of the brain that, if damaged, will result in loss of essential neurological function such …

The emergence of new data-and algorithm-driven analysis methods is revolutionizing many
areas of research and enabling solutions to problems that were previously considered …