Adversarial Image Synthesis for Unpaired Multi-Modal Cardiac Data

Abstract:
This paper demonstrates the potential for synthesis of medical images in one modality (e.g. MR) from images in another (e.g. CT) using a CycleGAN architecture. The synthesis can be learned from unpaired images, and applied directly to expand the quantity of available training data for a given task. We demonstrate the application of this approach in synthesising cardiac MR images from CT images, using a dataset of MR and CT images coming from different patients. Since there can be no direct evaluation of the synthetic images, as no ground truth images exist, we demonstrate their utility by leveraging our synthetic data to achieve improved results in segmentation. Specifically, we show that training on both real and synthetic data increases accuracy by 15% compared to real data. Additionally, our synthetic data is of sufficient quality to be used alone to train a segmentation neural network, that achieves 95% of the accuracy of the same model trained on real data.

Biography:

Agisilaos Chartsias received his diploma degree in Electronic Engineering and Computer Science from the Technical University of Crete, Greece, followed by a MSc in Artificial Intelligence from the University of Edinburgh. He is currently a PhD student in the University of Edinburgh under the supervision of Dr. Sotirios Tsaftaris working on deep learning for medical image analysis.