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

Morphological changes in knee cartilage subregions are valuable imaging‐based
biomarkers for understanding progression of osteoarthritis, and they are typically detected …

Objective To examine the performance of automated convolutional neuronal network
cartilage segmentation in knees with radiographic osteoarthritis (ROA), and its dependence …

Purpose To evaluate the performance of an ensemble learning approach for fully automated
cartilage segmentation on knee magnetic resonance images of patients with osteoarthritis …

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

Objective To assess automatic segmentations for surface-based analysis of cartilage
morphology and composition on knee magnetic resonance (MR) images. Methods 2D and …

Background Deep learning (DL)‐based automatic segmentation models can expedite
manual segmentation yet require resource‐intensive fine‐tuning before deployment on new …

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 …

Objective To evaluate the agreement, accuracy, and longitudinal reproducibility of
quantitative cartilage morphometry from 2D U-Net-based automated segmentations for 3T …

Purpose To organize a multi-institute knee MRI segmentation challenge for characterizing
the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring …