Synovitis is a very common finding in joints of RA patients, which may serve as biomarkers for early diagnosis and for early treatment response evaluation. However, synovitis quantification is challenging because manual segmentation of such irregular lesions is tedious and prone to inter reader variation. In this work, we implemented a fully automatic segmentation algorithm for synovitis lesions in wrist Magnetic Resonance images in subjects with RA using deep learning based conditional generative adversarial networks and U-Net. Using a small number of training data, the proposed model demonstrated feasibility of fully automatically synovitis segmentation with reasonable accuracy (Dice coefficient 0.78).
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