The 3D nature and soft-tissue contrast of MRI makes it an invaluable tool for osteoarthritis
research, by facilitating the elucidation of disease pathogenesis and progression. The recent …

Since the advent of U-Net, fully convolutional deep neural networks and its many variants
have completely changed the modern landscape of deep-learning based medical image …

Segmentation of knee articular cartilage tissue (ACT) from 3D magnetic resonance images
(MRIs) is a fundamental task in assessing knee osteoarthritis (KOA). However, automated …

The assessment of implant status and complications of Total Hip Replacement (THR) relies
mainly on the clinical evaluation of the X-ray images to analyse the implant and the …

Objective Magnetic resonance imaging is the standard imaging modality to assess articular
cartilage. As the imaging surrogate of degenerative joint disease, cartilage thickness is …

Semantic segmentation using convolutional neural networks (CNNs) is the state-of-the-art
for many medical image segmentation tasks including myocardial segmentation in cardiac …

Quantitative MRI techniques such as T2 and T1ρ mapping are beneficial in evaluating knee
joint pathologies; however, long acquisition times limit their clinical adoption. MIXTURE …

Abstract Clinical Magnetic Resonance Imaging (MRI) of joints is limited to mere morphologic
evaluation and fails to directly visualize joint or ligament function. In this controlled …

Objective: Statistical shape models have been successfully used in numerous biomedical
image analysis applications where prior shape information is helpful such as organ …

Für die Knorpelbildgebung im Rahmen degenerativer und nichtdegenerativer
Gelenkerkrankungen stellt die Magnetresonanztomographie (MRT) die klinische Methode …