In this study we evaluated the possibility of using transfer learning to improve the segmentation accuracy of femoral and tibial knee articular cartilage of a small locally acquired and annotated dataset. Two conditional Generative Adversarial Networks were trained - one with pretraining on the much larger SKI10 (Segmentation of Knee Images 2010) dataset and the other with random weight initialisation and no pretraining. Pretraining not only increased cartilage segmentation accuracy of the fine-tuned dataset, but also increased the network’s capacity to preserve segmentation capabilities for the pretrained dataset.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords