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Abstract #1020

Validation of Automatic Segmentation Algorithms of DWI in Acute Stroke Patients in Independent Data

Steven Mocking1, 2, Raquel Bezerra1, Elissa McIntosh1, Izzudin Diwan1, Priya Garg1, William Taylor Kimberly3, Ethem Murat Arseva1, Hakan Ay1, Aneesh B. Singhal3, William A. Copen4, Pamela Schaefer4, Ona Wu1

1Athinoula A. Martinos Center for Biomedical Imaging, Dept. of Radiology, MGH, Charlestown, MA, United States; 2Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands; 3Dept. of Neurology, MGH, Boston, MA; 4Dept. of Radiology, MGH, Boston, MA

We validated five algorithms for segmenting DWI lesions in acute ischemic stroke on an independent dataset and investigated the proportion of acutely misclassified tissue with follow-up manual outlines. Performance of the algorithms on the validation dataset was comparable to results on the training dataset, with the naive Bayes approach providing best sensitivity and Dice similarity coefficient. In many cases, several voxels classified as lesion or normal by the automatic algorithms but not by the manual outliner were found respectively to be infarcted or normal on follow-up imaging. Consequently, apparent misclassification may partially be a result of reader variation.