This contains an implementation of the SeGAN model for semantic segmentation introduced in https://arxiv.org/pdf/1706.01805.pdf

The model serves for semantic segmentation of image data and the authors have demonstrated its utility on cranial MRT images.

A summary of the model architecture from the paper is shown below

• Python 3.6
• Numpy
• Keras 2.0
• Tensorflow >= 1.x
• TQDM (optional)

This work was inspired by Xue et al. as well as the excellent "Deep Learning for coders" tought by Jeremy Howard and Rachel Thomas in their MOOC