From classical to deep learning: review on cartilage and bone segmentation techniques in knee osteoarthritis research
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From classical to deep learning: review on cartilage and bone segmentation techniques in knee osteoarthritis research Hong‑Seng Gan1 · Muhammad Hanif Ramlee2 · Asnida Abdul Wahab3 · Yeng‑Seng Lee4 · Akinobu Shimizu5
© Springer Nature B.V. 2020
Abstract Knee osteoarthritis is a major diarthrodial joint disorder with profound global socioeconomic impact. Diagnostic imaging using magnetic resonance image can produce morphometric biomarkers to investigate the epidemiology of knee osteoarthritis in clinical trials, which is critical to attain early detection and develop effective regenerative treatment/ therapy. With tremendous increase in image data size, manual segmentation as the standard practice becomes largely unsuitable. This review aims to provide an in-depth insight about a broad collection of classical and deep learning segmentation techniques used in knee osteoarthritis research. Specifically, this is the first review that covers both bone and cartilage segmentation models in recognition that knee osteoarthritis is a “whole joint” disease, as well as highlights on diagnostic values of deep learning in emerging knee osteoarthritis research. Besides, we have collected useful deep learning reviews to serve as source of reference to ease future development of deep learning models in this field. Lastly, we highlight on the diagnostic value of deep learning as key future computer-aided diagnosis applications to conclude this review. Keywords Bone segmentation · Cartilage segmentation · Knee osteoarthritis · Magnetic resonance imaging · Deep learning
* Hong‑Seng Gan [email protected] 1
Medical Engineering Technology Section, British Malaysian Institute, Universiti Kuala Lumpur, 53100 Gombak, Selangor, Malaysia
2
Department of Clinical Sciences, Medical Devices and Technology Group (MEDITEG), Faculty of Biosciences and Medical Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia
3
Faculty of Biosciences and Medical Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia
4
Bioelectromagnetics Research Group (BioEM), Department of Electronic Engineering Technology, Faculty of Engineering Technology, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia
5
Institute of Engineering, Tokyo University of Agriculture and Technology, 2‑24‑16, Naka‑cho, Koganei, Tokyo 184‑0012, Japan
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H.-S. Gan et al.
1 Introduction Knee Osteoarthritis (OA) is a whole joint disease (Loeser et al. 2012) caused by a multifactorial combination of biomechanical (Englund 2010), biochemical (Sokolove and Lepus 2013), systemic and intrinsic (Warner and Valdes 2016) risk factors. Often, the disease is associated with joint pain and progressive structural destruction of articular cartilage; and causes permanent physical impairment to the patients. In a recent literature update on OA epidemiology, knee OA has shown high prevalence rate across the globe (Vina and Kwoh 2018). Besides, various studies have highlighted the harmful effect of knee OA on our
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