A growing amount of imaging data is made publicly available. While this is desirable for science and its reproducibility, privacy concerns increase. As the shape of a face can be recovered based on MR images, an increased number of studies remove the face from the data to prevent biometric identification. This defacing can, however, pose a challenge to existing post-processing pipelines e.g. brain volume assessment. This work investigates the impact of regenerating facial structures in defaced images on morphometry in a large cohort using a deep neural network. The results show that refacing can prevent volumetric errors induced by defacing.
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