Abstract #2927

Algorithm for automated lesion segmentation in patients with familial Cerebral Cavernous Malformations(CCM)

Sivakami Avadiappan¹, Marc Mabray², Blaine L Hart², Melanie A Morrison¹, Angela Jakary¹, Leslie Morrison³, Atif Zafar³, Helen Kim^4,5, and Janine M Lupo¹

¹Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States, ²Department of Radiology, University of New Mexico, Albuquerque, NM, United States, ³Department of Neurology, University of New Mexico, Albuquerque, NM, United States, ⁴Department of Anesthesia and Perioperative Care, University of California, San Francisco, San Francisco, CA, United States, ⁵Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, United States

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.

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