Advancing deep learning based knee cartilage segmentation in MRI: Innovations, challenges and applications

Recent advancements in deep learning (DL) have advanced knee cartilage segmentation in Magnetic Resonance Imaging (MRI), offering scalable, automated solutions that markedly reduce reader time and address the limitations of traditional manual approaches. Automated segmentation can substantially aid osteoarthritis (OA) assessment using MRI, facilitating consistent, reproducible quantification across large longitudinal cohorts, reduces inter-/intra-observer variability, capabilities that are impractical with manual workflows.

This study presents a concise review of state-of-the-art DL-based approaches for knee cartilage segmentation, focusing on the evaluation of various architectures, techniques, and their adaptability to diverse datasets and imaging protocols. This review highlights key challenges in knee cartilage segmentation, including data scarcity, domain shifts, and imaging variability, while also discussing proposed solutions such as semi-supervised learning, domain adaptation, augmentation strategies, and foundation models. Additionally, the clinical significance of knee cartilage segmentation is underscored through its diverse applications.

The study highlights substantial improvements against conventional methods in segmentation accuracy and efficiency using DL-based methods, given challenging scenarios of knee MRI. Solutions to key challenges are presented, and clinical applications showcase the potential of automated segmentation for cartilage thickness mapping and OA assessment.

DL-based segmentation is advancing musculoskeletal imaging by offering reliable and automated solutions. Despite persistent challenges such as data scarcity, domain shifts, and imaging variability, advancements in areas like semi-supervised learning, domain adaptation, augmentation strategies, and foundation models present significant opportunities to enhance model robustness and expand clinical applicability.