Generative Model of Brain Microbleeds for MRI Detection of Vascular Marker of Neurodegenerative Diseases - Momeni, Saba; Fazlollahi, Amir; Lebrat, Leo; Yates, Paul; Rowe, Christopher; Gao, Yongsheng; Liew, Alan Wee-Chung; Salvado, Olivier (Frontiers in Neuroscience, 2021)
Cerebral microbleeds (CMB) are increasingly present with ageing and can reveal vascular pathologies associated with neurodegeneration. Deep learning-based classifiers can detect and quantify CMB from MRI such as susceptibility imaging but are challenging to train because of the limited availability of ground truth and many confounding imaging features such as vessels or infarcts. In this study, we present a novel Generative Adversarial Network that has been trained to generate 3 dimensional lesions, conditioned by volume and location. This allows to investigate CMB characteristics and create large training datasets for deep learning-based detectors. We demonstrate the benefit of this approach by achieving state of the art CMB detection of real CMB using a convolutional neural network classifier trained on synthetic CMB. Moreover, we showed that our proposed 3D lesion GAN model can be applied on unseen dataset, with different MRI parameters and diseases, to generate synthetic lesions with high diversity without needing laboriously marked ground truth.