Comparative Study of Encoder-decoder-based Convolutional Neural Networks in Cartilage Delineation from Knee Magnetic Resonance Images

(E-pub Ahead of Print)

Author(s): Ching Wai Yong, Khin Wee Lai*, Belinda Pingguan Murphy, Yan Chai Hum

Journal Name: Current Medical Imaging
Formerly: Current Medical Imaging Reviews


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Abstract:

Background: Osteoarthritis (OA) is a common degenerative joint inflammation which may lead to disability. Although OA is not lethal, this disease will remarkably affect patient’s mobility and their daily lives. Detecting OA at an early stage allows for early intervention and may slow down disease progression. Introduction: Magnetic resonance imaging is a useful technique to visualize soft tissues within the knee joint. Cartilage delineation in magnetic resonance (MR) images helps in understanding the disease progressions. Convolutional neural networks (CNNs) have shown promising results in computer vision tasks, and various encoder–decoder-based segmentation neural networks are introduced in the last few years. However, the performances of such networks are unknown in the context of cartilage delineation.

Methods: This study trained and compared 10 encoder–decoder-based CNNs in performing cartilage delineation from knee MR images. The knee MR images are obtained from Osteoarthritis Initiative (OAI). The benchmarking process is to compare various CNNs based on the physical specifications and segmentation performances.

Results: LadderNet has the least trainable parameters with model size of 5 MB. UNetVanilla crowned the best performances by having 0.8369, 0.9108, and 0.9097 on JSC, DSC, and MCC.

Conclusion: UNetVanilla can be served as a benchmark for cartilage delineation in knee MR images while LadderNet served as alternative if there are hardware limitations during production.

Keywords: Comparative study, convolutional neural network, encoder-decoder neural network, knee cartilage segmentation, magnetic resonance imaging, osteoarthritis

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(E-pub Ahead of Print)
DOI: 10.2174/1573405616666201214122409

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