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.
Automated analysis of cardiovascular magnetic resonance myocardial native T1 mapping images using fully convolutional neural networks
