DEEPMIR: A DEEP convolutional neural network for differential detection of cerebral Microbleeds and IRon deposits in MRI

Background: Cerebral microbleeds (CMBs) and non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Recent advances using quantitative susceptibility mapping (QSM) make it possible to differentiate iron content from mineralization in-vivo using magnetic resonance imaging (MRI). However, automated detection of such lesions is still challenging, making quantification in large cohort bases studies rather limited. Purpose: Development of a fully automated method using deep learning for detecting CMBs and basal ganglia iron deposits using multimodal MRI. Materials and Methods: We included a convenience sample of 24 participants from the MESA cohort and used T2-weighted images, susceptibility weighted imaging (SWI), and QSM to segment the lesions. We developed a protocol for simultaneous manual annotation of CMBs and non-hemorrhage iron deposits in the basal ganglia, which resulted in defining the gold standard. This gold standard was then used to train a deep convolution neural network (CNN) model. Specifically, we adapted the U-Net model with a higher number of resolution layers to be able to detect small lesions such as CMBs from standard resolution MRI which are used in cohort-based studies. The detection performance was then evaluated using the cross-validation principle in order to ensure generalization of the results. Results: With multi-class CNN models, we achieved an average sensitivity and precision of about 0.8 and 0.6, respectively for detecting CMBs. The same framework detected non-hemorrhage iron deposits reaching an average sensitivity and precision of about 0.8. Conclusions: Our results showed that deep learning could automate the detection of small vessel disease lesions and including multimodal MR data such as QSM can improve the detection of CMB and non-hemorrhage iron deposits.