Medical Image Synthesis using Generative Adversarial Networks (GANs)
Supervision: Prof. Dr. Nassir Navab
, Shadi Albarqouni
, Christoph Baur
Magnetic resonance imaging (MRI) enables the acquisition of the underlying anatomy with different protocols, i.e. T1-weighted and T2-weighted, PD and FLAIR. Recent studies  show the importance of enhanced contrast image, namely double inversion recovery (DIR), which can be used for MS Lesion Segmentation. In this project, we will investigate the Medical Image Synthesis of DIR using other protocols (might be few of them ).
- Good understanding of medical physics
- Good understanding of statistics and machine learning methods.
- Very good programming skills in Python & TensorFlow? / pyTorch
- Willingness to closely collaborate with physicians and experts in machine learning.
 Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. and Bengio, Y., 2014. Generative adversarial nets
. In Advances in Neural Information Processing Systems (pp. 2672-2680).
 Denton, E.L., Chintala, S. and Fergus, R., 2015. Deep Generative Image Models using a￼ Laplacian Pyramid of Adversarial Networks
. In Advances in neural information processing systems (pp. 1486-1494).
 Nie, D., Trullo, R., Petitjean, C., Ruan, S. and Shen, D., 2016. Medical Image Synthesis with Context-Aware Generative Adversarial Networks
. arXiv preprint arXiv:1612.05362.
 Havaei, M., Guizard, N., Chapados, N. and Bengio, Y., 2016, October. HeMIS: Hetero-Modal Image Segmentation
. In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) (pp. 469-477).
 Wiggermann, V., Hernández-Torres, E., Traboulsee, A., Li, D.K.B. and Rauscher, A., 2016. FLAIR2: A combination of FLAIR and T2 for improved MS lesion detection
. American Journal of Neuroradiology, 37(2), pp.259-265.