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Abstract #0546

Don’t Lose Your Face - Refacing for Improved Morphometry

Till Huelnhagen1,2,3, Mário João Fartaria1,2,3, Ricardo Corredor-Jerez1,2,3, Mazen Fouad A. Wali Mahdi1, Gian Franco Piredda1,2,3, Bénédicte Maréchal1,2,3, Jonas Richiardi1,2, and Tobias Kober1,2,3
1Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, Lausanne, Switzerland, 2Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, 3LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

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.

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