In this study, we developed a method for compartment-specific segmentation of knee cartilage from 3D-DESS MR images which jointly utilizes deep learning and atlas-based approaches. The method was applied to compare the performance of two deep learning-based segmentation models on two independent datasets. One of the models achieved new state-of-the-art in knee cartilage segmentation on the Osteoarthritis Initiative data and was more robust to the changes in MRI protocol. Detailed analysis performed using our method showed how the performance improvements are localized compartment-wise. The method can be used to select the most accurate segmentation model for the considered clinical problem.
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