Generative Model of Brain Microbleeds for MRI Detection of Vascular Marker of Neurodegenerative Diseases.

2021-12-16

Momeni, Saba

Fazlollahi, Amir

Lebrat, Leo

Yates, Paul A

Rowe, Christopher C

Gao, Yongsheng

Liew, Alan Wee-Chung

Salvado, Olivier

SWI images

cerebral microbleed

data augmentation

deep learning

generative adversarial network

synthetic data

Journal Article

0000-0001-9317-0145

0000-0003-3910-2453

0000-0002-2720-8739

10.3389/fnins.2021.778767

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.

link

Frontiers in Neuroscience 2021; 15: 778767

Frontiers in Neuroscience

1662-4548