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Abstract #3252

Defacing and Refacing Brain MRI Using a Cycle Generative Adversarial Network

Zuojun Wang1, Peng Xia1, Wenming Cao2, Kui Kai, Gary Lau1, Henry Ka Fung Mak3, and Peng Cao1
1Diagnostic Radiology, Department of Diagnostic Radiology, HKU, Hong Kong, China, 2Department of Diagnostic Radiology, HKU, HongKong, China, 3Department of Diagnostic Radiology, HKU, Hong Kong, China

MRI anonymizations, including face removal, are necessary for clinical data archiving and sharing. Segmentation based methods have been developed for semi-automated face removal on brain MRI. Meanwhile, the conventional methods are inefficient and unreliable, as the images have to be pre-processed and fed in the software manually. Deep learning-based methods are highly efficient in image-to-image translation on large scale databases. In this study, we utilized a cycle generative adversarial network to anonymize brain MRI data. The model showed reliable performance when testing on T1-weighted images, and we also extend it to the unseen MPRAGE images, targeting different brain MRI contrasts.

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