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. Author manuscript; available in PMC: 2021 Sep 19.
Published in final edited form as: Curr Opin Neurobiol. 2019 Mar 15;55:112–120. doi: 10.1016/j.conb.2019.02.005

Figure 3: Generative models for synthesizing structural brain images.

Figure 3:

On the left, we depict an autoencoder consisting of an input layer, a low-dimensional hidden layer (latent space), and output layer. In the training phase, a low-dimensional model for data is learned and in the synthesis phase, a sample from this model is generated and used to generate a new image. This architecture is applied to auto-fluorescence images of 1,700 different brains (25 micron resolution) to synthesize new images: on the right, a synthetically generated image (top), example of a real image used to train the network (bottom), and a denoised (reconstructed) version of the image displayed on the bottom.