Deep Generative Adversarial Neural Networks for Realistic Prostate Lesion MRI Synthesis

Understand

Generative Adversarial Neural Networks (GANs) are applied to the synthetic generation of prostate lesion MRI images.

• GANs have been applied to a variety of natural images, is shown show that the same techniques can be used in the medical domain to create realistic looking synthetic lesion images.
• 16mm x 16mm patches are extracted from 330 MRI scans from the SPIE ProstateX Challenge 2016 and used to train a Deep Convolutional Generative Adversarial Neural Network (DCGAN) utilizing cutting edge techniques.
• Synthetic outputs are compared to real images and the implicit latent representations induced by the GAN are explored.