Refacing Defaced MRI with PixelCNN

Privacy protection is one of the most crucial factors when sharing MR images between researchers. There are many defacing software programs that can blur or remove the face of MR images. However, there are reasons to believe that the brain and other remaining features are not only identifiable but also can be used for facial reconstruction to fill the face of the subject back into the image, and it is possible to rebuild the facial part of the images using recently developed machine learning models. A demonstration of this is of practical significance as it could convince the community to adopt stricter data-sharing standards. Additionally, even if the reconstructed faces are not identifiable, smooth completion of the “damaged” MRI images may improve methods that depend on the head modeling, such as source localization approaches. Recent work has been focusing on using the generative adversarial networks (GAN) for this task, which is generally believed to be the best method. Because we hope the model can generate the face entirely depending on the information from the brain, we show here an alternative approach, pixel constrained CNN, which is a purely supervised facial reconstruction. We simulated the rebuild process and showed convincing results.