Automatic segmentation of knee menisci - A systematic review

Area of Science:

• Orthopedics
• Radiology
• Medical Imaging Analysis

Background:

• Osteoarthritis pathogenesis research heavily relies on Magnetic Resonance Imaging (MRI).
• Accurate meniscus segmentation in MR images is challenging due to similar signal intensities from surrounding tissues and significant inter-individual variations in meniscus size and shape.
• Effective meniscus segmentation is vital for quantitative analysis and understanding knee joint pathologies.

Purpose of the Study:

• To systematically review fully automatic and semi-automatic knee meniscus segmentation methods.
• To provide a comprehensive overview of advancements in automated meniscus segmentation techniques.
• To guide clinicians and researchers in developing novel automated methods for clinical applications.

Main Methods:

• Systematic literature review adhering to the PRISMA statement.
• Inclusion of published scientific articles detailing automatic and semi-automatic meniscus segmentation algorithms.
• Categorization and description of identified segmentation methods.

Main Results:

• An overview of various automated and semi-automated algorithms developed over the past two decades for knee meniscus segmentation.
• Identification of key advancements in MRI technology and computational methods driving these developments.
• Summary of the current landscape of meniscus segmentation techniques.

Conclusions:

• Automated meniscus segmentation is essential for efficient and accurate osteoarthritis research.
• Continued development of automated segmentation methods is needed for widespread clinical adoption.
• This review serves as a resource for future research in knee meniscus segmentation.