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.
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