Publications by authors named "Hollis A Crowder"
Article Synopsis
- Deep learning (DL) models for automatic segmentation can save time but need extensive fine-tuning for new datasets; this study evaluates their ability to work without such adjustments.
- The researchers tested pretrained DL models on different datasets that varied by scanner type and patient characteristics, including healthy individuals and those with knee issues.
- Results showed that the DL model trained specifically on qDESS data outperformed the one trained on Osteoarthritis Initiative data in terms of segmentation accuracy and agreement with manual measurements.
Cartilage transmits and redistributes biomechanical loads in the knee joint during exercise. Exercise-induced loading alters cartilage hydration and is detectable using quantitative magnetic resonance imaging (MRI), where T relaxation time (T ) is influenced by cartilage collagen composition, fiber orientation, and changes in the extracellular matrix. This study characterized short-term transient responses of healthy knee cartilage to running-induced loading using bilateral scans and image registration.
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