Cerebral microbleed detection on susceptibility weighted imaging using solely artificial training data

Motivation: Cerebral microbleeds (CMBs) are small brain hemorrhages detectable with MRI associated with conditions like cerebral amyloid angiopathy. As their detection can be difficult, automated methods are needed for quick and precise detection and localization of CMBs. Goal(s): To propose an algorithm to detect CMBs. Approach: A neural network was trained on SWI/T2* images, with artificial bleeds generated and added during training. The model’s performance was tested on an independent test set with actual CMBs. Results: Despite the absence of real CMBs in the training data, the simulated bleeds provided sufficient information to train a model with good performance in the independent test set. Impact: We propose an algorithm that can help with the tedious radiological task of detecting cerebral microbleeds in the brain. We further demonstrate that a model trained solely on simulated bleeds can effectively detect actual microbleeds in real MRI data.