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A Comprehensive Survey on Bone Segmentation Techniques in Knee Osteoarthritis Research: From Conventional Methods to
Sozan Mohammed Ahmed1, Ramadhan J Mstafa1,2
1Department of Computer Science, Faculty of Science, University of Zakho, Duhok 42002, Iraq.
Diagnostics (Basel, Switzerland)
|March 25, 2022
Summary
Accurate knee osteoarthritis (KOA) prediction is crucial for early diagnosis and preventing joint replacements. This review examines segmentation methods, from traditional to deep learning, aiding KOA assessment and drug development.
Area of Science:
- Orthopedics and Medical Imaging
- Biomedical Engineering
- Artificial Intelligence in Medicine
Background:
- Knee osteoarthritis (KOA) is a prevalent degenerative joint disease affecting middle-aged and elderly populations, primarily involving hyaline cartilage changes.
- Accurate early diagnosis of KOA is challenged by technical issues in medical imaging, such as noise, artifacts, and varying modalities.
- Effective KOA prediction is vital for timely treatment, slowing disease progression, and potentially averting millions of annual joint replacement surgeries.
Purpose of the Study:
- To provide a comprehensive review of recent knee articular bone segmentation methodologies for KOA diagnosis.
- To analyze traditional and deep learning (DL)-based techniques used in estimating articular cartilage loss rate.
- To offer researchers a consolidated overview of existing methods, highlighting clinical application deficiencies and future potential.
Main Methods:
- Review of traditional segmentation techniques for knee articular bone.
- Analysis of deep learning (DL)-based methodologies for knee articular bone segmentation.
- Evaluation of segmentation's role in assessing articular cartilage loss and disease progression.
Main Results:
- Segmentation methods are critical for KOA diagnosis, categorization, and assessing cartilage loss rate.
- Deep learning techniques show significant promise for improving the accuracy and efficiency of knee articular bone segmentation.
- The review synthesizes current methodologies, identifying gaps for future research and clinical implementation.
Conclusions:
- Articular bone segmentation is essential for objective KOA assessment and monitoring disease progression.
- Deep learning approaches offer advanced capabilities for computer-aided diagnosis in KOA.
- Further research into DL-based segmentation can accelerate the development of disease-modifying drugs and improve clinical outcomes for KOA patients.
