Purpose To analyze how automatic segmentation translates in accuracy and precision to
morphology and relaxometry compared with manual segmentation and increases the speed …

Background Semiquantitative assessment of MRI plays a central role in musculoskeletal
research; however, in the clinical setting MRI reports often tend to be subjective and qualitative. …

Osteoarthritis (OA) classification in the knee is most commonly done with radiographs using
the 0–4 Kellgren Lawrence (KL) grading system where 0 is normal, 1 shows doubtful signs …

Convolutional neural networks for semantic segmentation suffer from low performance at
object boundaries. In medical imaging, accurate representation of tissue surfaces and volumes …

Purpose To develop and compare with the classical region of interest (ROI)‐based approach
a fully automatic, local, and unbiased way of studying the knee T 1ρ relaxation time by …

Purpose To investigate the feasibility of automatic identification and classification of hip
fractures using deep learning, which may improve outcomes by reducing diagnostic errors and …

Purpose To test the hypothesis that artificial intelligence (AI) techniques can aid in identifying
and assessing lesion severity in the cartilage, bone marrow, meniscus, and anterior …

Objective To evaluate the longitudinal reproducibility and variations of cartilage T 1ρ and T
2 measurements using different coils, MR systems and sites. Methods Single-Site study: …

Knee Osteoarthritis (OA) is a common musculoskeletal disorder in the United States. When
diagnosed at early stages, lifestyle interventions such as exercise and weight loss can slow …

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 …