Meeting Banner
Abstract #2938

Automatic Segmentation of Diffusion MRI from the Genes Associated with Stroke Risk and Outcomes Study

Steven Mocking1, Natalia S. Rost2, Kaitlin M. Fitzpatrick2, Allison Kanakis2, Lisa Cloonan2, Jonathan Rosand2, Karen L. Furie3, Ona Wu1

1Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States; 2Department of Neurology, Massachusetts General Hospital, Boston, MA, United States; 3Department of Neurology, Brown University, Providence, RI, United States

Automated Algorithms for segmenting ischemic stroke lesions in diffusion MRI based on ADC thresholding and Naive Bayes classification are evaluated against manual outlines in independent data from stroke patients seen with 48 h of admission. Manual outlines took approximately 5-30 minutes/subject. Naive Bayes significantly outperformed ADC thresholding in terms of voxel-wise sensitivity and Dice similarity metric. Both automated algorithms took 20-40s/subject.Genome wide association studies seeking to link genetic variants with imaging phenotypes that require thousands of subjects would benefit from automated lesion segmentation techniques.