The number and location of cerebral microbleeds (CMBs) in patients with traumatic brain injury (TBI) is
important to determine the severity of trauma and may hold prognostic value for patient outcome. However,
manual assessment is subjective and time-consuming due to the resemblance of CMBs to blood vessels, the
possible presence of imaging artifacts, and the typical heterogeneity of trauma imaging data. In this work, we
present a computer aided detection system based on 3D convolutional neural networks for detecting CMBs in 3D
susceptibility weighted images. Network architectures with varying depth were evaluated. Data augmentation
techniques were employed to improve the networks’ generalization ability and selective sampling was implemented
to handle class imbalance. The predictions of the models were clustered using a connected component analysis.
The system was trained on ten annotated scans and evaluated on an independent test set of eight scans. Despite
this limited data set, the system reached a sensitivity of 0.87 at 16.75 false positives per scan (2.5 false positives
per CMB), outperforming related work on CMB detection in TBI patients.