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

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Orthopedics

Background:

• Manual knee cartilage segmentation in MRI is time-consuming and prone to variability.
• Osteoarthritis (OA) assessment using MRI requires consistent and reproducible cartilage quantification.
• Deep learning (DL) offers scalable, automated solutions to overcome limitations of manual segmentation.

Purpose of the Study:

• To review state-of-the-art DL-based approaches for knee cartilage segmentation.
• To evaluate various DL architectures, techniques, and their adaptability to diverse MRI datasets and protocols.
• To highlight challenges and solutions in DL-based knee cartilage segmentation for OA assessment.

Main Methods:

• Review of recent DL advancements for knee cartilage segmentation.
• Focus on evaluation of different DL architectures and techniques.
• Discussion of adaptability to diverse datasets and imaging protocols.

Main Results:

• DL methods show substantial improvements in segmentation accuracy and efficiency over conventional methods for knee MRI.
• Key challenges like data scarcity, domain shifts, and imaging variability are addressed.
• Clinical applications demonstrate potential for cartilage thickness mapping and OA assessment.

Conclusions:

• DL-based segmentation is advancing musculoskeletal imaging with reliable, automated solutions.
• Despite challenges, advancements in semi-supervised learning, domain adaptation, and foundation models enhance robustness.
• These advancements expand the clinical applicability of automated knee cartilage segmentation for OA management.