Cerebral cavernous malformations (CCMs) are collections of small blood vessels in the brain that are enlarged and irregular in structure and have clinical manifestations that include seizures and hemorrhage. In this work, we developed and evaluated an automated algorithm for counting and quantifying different sized CCM lesions on SWI images. The total lesion burden increased with overall symptom score in baseline scans from 50 patients. Large lesion burden increased at follow-up in 16/17 cases. Our automated algorithm is a consistent method for counting microbleeds and accurate volume estimation and can thus facilitate lesion burden calculation and tracking in CCM patients.