Brain MRI Synthesis Using Stylegan2-ADA

Deep learning (DL) applications in the medical field often face challenges related to limited data availability, resulting in issues like overfitting and imbalanced datasets. Synthetic data offers a promising solution to these problems by enabling data augmentation and enhancing the performance of DL models. In this study, we trained the state-of-the-art generative model StyleGAN2-ADA on 1412 images from the Alzheimer's disease neuroimaging initiative (ADNI) dataset to generate synthetic slices of T1-weighted brain MRI of healthy subjects. The quality of the synthetic images has been evaluated through quantitative and qualitative assessments, including a visual Turing test conducted by an expert observer with 2000 images. The observer achieved an accuracy of 52.95%, indicative of a performance level comparable to random guessing. These results demonstrate the capability of StyleGAN2-ADA to generate anatomically relevant synthetic brain MRI data.