Objective. Accurate automated segmentation of cartilage should provide rapid reliable
outcomes for both epidemiological studies and clinical trials. We aimed to assess the …

INTRODUCTION: Quantitative cartilage morphometry is a valuable tool to assess
osteoarthritis (OA) progression. Current methodologies generally evaluate cartilage …

Objective To assess the responsiveness and reader time of a novel semiautomated tool to
detect knee cartilage loss over 2 years in subjects with knee osteoarthritis. Methods A total of …

Objective To study the longitudinal performance of fully automated cartilage segmentation in
knees with radiographic osteoarthritis (OA), we evaluated the sensitivity to change in …

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

Background The variation in articular cartilage thickness (ACT) in healthy knees is difficult to
quantify and therefore poorly documented. Our aims are to (1) define how machine learning …

Many studies have validated cartilage thickness as a biomarker for knee osteoarthritis (OA);
however, few studies investigate beyond cross‐sectional observations or comparisons …

Background The cartilage segmentation algorithms make it possible to accurately evaluate
the morphology and degeneration of cartilage. There are some factors (location of cartilage …

Objective: Understanding how knee cartilage is affected by osteoarthritis (OA) is critical in
the development of sensitive biomarkers that may be used as surrogate endpoints in clinical …

Purpose To determine the reproducibility of the automatic cartilage segmentation method
using a prototype KneeCaP software (version 1.3; Siemens Healthcare, Erlangen, Germany) …