Generative MRI model of brain microbleeds for detection of vascular marker of neurodegenerative diseases

Saba Momeni, Amir Fazlollahi, Leo Lebrat, Paul Yates, Christopher Rowe, Yongsheng Gao, Alan Wee-Chung Liew, Olivier Salvado

Abstract:

Cerebral microbleeds (CMB) are increasingly present with aging 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 (GAN) that has been trained to generate three-dimensional lesions,
conditioned by volume and location. This allows one 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
and without needing laboriously marked ground truth.