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Abstract #4808

Automated Knee MRI Semantic Segmentation with Generative Adversarial Networks

Dimitri A Kessler1, James W MacKay1, Martin J Graves1, Fiona J Gilbert1, and Joshua D Kaggie1

1Department of Radiology, University of Cambridge, Cambridge, United Kingdom

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 (nimages=778) to generate segmentation maps containing the patella, femur and tibia. The trained network was tested with a separate dataset of one individual (nimages=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.

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