Mask Embedding for Realistic High-Resolution Medical Image Synthesis

Generative Adversarial Networks (GANs) have found applications in natural image synthesis and begin to show promises generating synthetic medical images. In many cases, the ability to perform controlled image synthesis using masked priors such as shape and size of organs is desired. However, mask-guided image synthesis is challenging due to the pixel level mask constraint. While the few existing mask-guided image generation approaches suffer from the lack of fine-grained texture details, we tackle the issue of mask-guided stochastic image synthesis via mask embedding. Our novel architecture first encodes the input mask as an embedding vector and then inject these embedding into the random latent vector input. The intuition is to classify semantic masks into partitions before feature up-sampling for improved sample space mapping stability. We validate our approach on a large dataset containing 39,778 patients with 443,556 negative screening Full Field Digital Mammography (FFDM) images. Experimental results show that our approach can generate realistic high-resolution (256x512) images with pixel-level mask constraints, and outperform other state-of-the-art approaches.