Background: Osteoarthritis (OA) is a common degenerative joint inflammation that
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
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 the Osteoarthritis
Initiative (OAI). The benchmarking process is to compare various CNNs based on physical specifications
and segmentation performances.
Results: LadderNet has the least trainable parameters with the 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 an alternative if there are hardware limitations during production.