Exploring Medical Image Data Augmentation and Synthesis using conditional Generative Adversarial Networks

Abstract

Obtaining healthcare data such as magnetic resonance imaging data for medical diagnosis is expensive and time-consuming. In this thesis, a method using generative adversarial networks is explored for synthesizing data of brain gliomas to improve the performance of image segmentation algorithms. The network was trained to create subjects with gliomas from a given label, and the network is able to synthesize visible tumors. The data was evaluated using DeepMedic, an image segmentation convolutional neural network. The performance of the model on the augmented dataset was benchmarked against the unaugmented dataset, and its performance was not improved. An analysis on the data is presented, and a future direction is given for how the generative adversarial network can be improved.