Automated Knee MRI Semantic Segmentation with Generative Adversarial Networks

We describe a fully automated deep learning approach for generating semantic segmentation maps of the knee joint. A conditional Generative Adversarial Network (cGAN) was trained on 3D fat-saturated spoiled gradient recalled-echo MRIs of the knee from nine individuals (n_images=778) to generate segmentation maps containing the patella, femur and tibia. The trained network was tested with a separate dataset of one individual (n_images=80). The mean Sørensen–Dice Similarity Coefficient (DSC) was 0.959 and Jaccard Index was 0.985 for all three compartments. These results suggest that cGANs can perform accurate bony segmentation of the knee.