Abstract #0432

Automatic detection of cerebral microbleeds using Susceptibility-Weighted Imaging and a 3D deep residual network

Yicheng Chen¹, Melanie Morrison², Javier Villanueva-Meyer², and Janine M Lupo^1,2

¹The UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, San Francisco, CA, United States, ²Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States

In this abstract, a deep residual neural network based approach that improves the automatic detection and labeling of cerebral microbleeds by significantly reducing the number of false positives compared to previously published algorithms is proposed. This combined method removed 89% of false positives in the test patients with brain tumors who had radiation-induce CMBs while losing only 3% of the true microbleeds and has the potential to fully automate CMB detection.

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