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A Review on Segmentation of Knee Articular Cartilage: from Conventional Methods Towards Deep Learning

Ebrahimkhani, Somayeh and Jaward, Mohamed Hisham and Cicuttini, Flavia M and Dharmaratne, Anuja and Wang, Yuanyuan and Garcia Seco De Herrera, Alba (2020) 'A Review on Segmentation of Knee Articular Cartilage: from Conventional Methods Towards Deep Learning.' Artificial Intelligence in Medicine, 106. p. 101851. ISSN 0933-3657

A review on segmentation of knee articular cartilage from conventional methods.pdf - Accepted Version
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In this paper, we review the state-of-the-art approaches for knee articular cartilage segmentation from conventional techniques to deep learning (DL) based techniques. Knee articular cartilage segmentation on magnetic resonance (MR) images is of great importance in early diagnosis of osteoarthritis (OA). Besides, segmentation allows estimating the articular cartilage loss rate which is utilised in clinical practice for assessing the disease progression and morphological changes. Topics covered include various image processing algorithms and major features of different segmentation techniques, feature computations and the performance evaluation metrics. This paper is intended to provide researchers with a broad overview of the currently existing methods in the field, as well as to highlight the shortcomings and potential considerations in the application at clinical practice. The survey showed that the state-of-the-art techniques based on DL outperforms the other segmentation methods. The analysis of the existing methods reveals that integration of DL-based algorithms with other traditional model-based approaches have achieved the best results (mean Dice similarity cofficient (DSC) between 85:8% and 90%).

Item Type: Article
Uncontrolled Keywords: Knee osteoarthritis (OA); Articular cartilage segmentation; Magnetic resonance imaging (MRI); Medical image analysis; Deep convolutional neural network (CNN)
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 04 May 2020 07:37
Last Modified: 23 Sep 2022 19:37

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