Meeting Banner
Abstract #1421

Automated Cartilage and Meniscus Segmentation of Knee MRI with Conditional Generative Adversarial Nets

Sibaji Gaj1, Mingrui Yang1, Kunio Nakamura1, and Xiaojuan Li1

1Department of Biomedical Engineering, Cleveland Clinic, Cleveland, OH, United States

Clinical translation of quantitative MRI techniques requires accurate cartilage and tissue segmentation. In this work, we have developed and tested a fully automated cartilage and meniscus segmentation model for knee joint using deep learning. To improve segmentation performance by incorporating multi-scale spatial constraints in objective function, the proposed model combines CGAN and U-Net and the dice and cross-entropy loss are added to the CGAN’s objective function. The segmentation performance has been improved for all six compartments and the average dice coefficient for segmentation during testing is 0.88 compared to 0.79 of existing U-Net based segmentation.

This abstract and the presentation materials are available to members only; a login is required.

Join Here