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Automatic segmentation of knee menisci - A systematic review
Muhammed Masudur Rahman1, Lutz Dürselen1, Andreas Martin Seitz1
1Institute of Orthopedic Research and Biomechanics, Ulm University Medical Center, Helmholtzstr. 14, 89081 Ulm, Germany.
Abstract:
Magnetic resonance imaging (MRI) has proved to be an invaluable component of pathogenesis research in osteoarthritis. Nevertheless, the detection of a meniscal lesion from magnetic resonance (MR) images is always challenging for both clinicians and researchers, because the surrounding tissues lead to similar signals within MR measurements, thus being difficult to discriminate. Moreover, the size and shape of osteoarthritic and non-osteoarthritic menisci vary to a large extent between individuals of same features, e.g. height, weight, age, etc. An effective way to visualize the entire volume of knee menisci is to segment the menisci voxels from the MR images, which is also useful to evaluate particular properties quantitatively. However, segmentation is a tedious and time-consuming task, and requires adequate training for being done properly. With the advancement of both MRI technology and computer methods, researchers have developed several algorithms to automate the task of meniscus segmentation of the individual knee during the last two decades. The objective of this systematic review was to present available fully automatic and semi-automatic segmentation methods of the knee meniscus published in different scientific articles according to the PRISMA statement. This review should provide a vivid description of the scientific advancements to clinicians and researchers in this field to help developing novel automated methods for clinical applications.
Insights
Automating knee meniscus segmentation from MRI scans is crucial for osteoarthritis research. This review details current automatic and semi-automatic methods to aid in developing advanced clinical applications.
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.
