Cartilage Segmentation

Cartilage segmentation, the automated identification and delineation of cartilage in medical images, aims to improve the accuracy and efficiency of osteoarthritis diagnosis and treatment planning. Current research heavily utilizes deep learning, particularly variations of U-Net architectures and transformer networks, to achieve robust and precise segmentation across different cartilage types (knee, costal, wrist) and imaging modalities (MRI, CT, ultrasound). These advancements enable quantitative analysis of cartilage morphology, including thickness, volume, and lesion detection, leading to improved biomarkers for disease assessment and facilitating personalized interventions.