  Lobar cerebral microbleeds (CMBs) and localized non-hemorrhage iron deposits
in the basal ganglia have been associated with brain aging, vascular disease
and neurodegenerative disorders. Particularly, CMBs are small lesions and
require multiple neuroimaging modalities for accurate detection. Quantitative
susceptibility mapping (QSM) derived from in vivo magnetic resonance imaging
(MRI) is necessary to differentiate between iron content and mineralization. We
set out to develop a deep learning-based segmentation method suitable for
segmenting both CMBs and iron deposits. 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 two types of lesions. We
developed a protocol for simultaneous manual annotation of CMBs and
non-hemorrhage iron deposits in the basal ganglia. This manual annotation was
then used to train a deep convolution neural network (CNN). 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. We tested
different combinations of the three modalities to determine the most
informative data sources for the detection tasks. In the detection of CMBs
using single class and multiclass models, we achieved an average sensitivity
and precision of between 0.84-0.88 and 0.40-0.59, respectively. The same
framework detected non-hemorrhage iron deposits with an average sensitivity and
precision of about 0.75-0.81 and 0.62-0.75, respectively. Our results showed
that deep learning could automate the detection of small vessel disease lesions
and including multimodal MR data (particularly QSM) can improve the detection
of CMB and non-hemorrhage iron deposits with sensitivity and precision that is
compatible with use in large-scale research studies.
