Cerebral microbleeds (CMBs) are known risk factors of stroke and hemorrhage that can be a marker of cognitive impairment. Although CMBs are easily visualized with susceptibility weighted imaging, they are burdensome to localize and quantify manually. Image processing algorithms based on the radial symmetry transform have previously been used to identify candidate CMBs, and convolutional neural networks have been effective at distinguishing real CMBs from mimics with high sensitivity and specificity. A deep neural network was trained to carry out this entire pipeline and to predict CMB voxel masks using a dataset of radiation therapy-induded CMBs from patients with gliomas.
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