Abstract #3315

A Generic Supervised Learning Framework for Fast Brain Extraction

Yuan Liu^1,2, Benjamin Odry², Hasan Ertan Cetingul², and Mariappan Nadar²

¹Vanderbilt Institute in Surgery and Engineering, Vanderbilt University, Nashville, TN, United States, ²Medical Imaging Technologies, Siemens Healthcare, Princeton, NJ, United States

Automatic brain extraction, as a standard pre-processing step, typically suffers from a long runtime and inaccuracies caused by brain variations and limited qualities of MR images. We propose a generic supervised learning framework that builds binary classifiers to identify brain and non-brain tissues at different resolution levels, hierarchically performs voxel-wise classifications for a test subject, and refines the brain boundary using narrow-band level set technique on the classification map. The proposed method is evaluated on multiple datasets with different acquisition sequences and scanner types using uni- or multi-contrast images and shown to be fast, accurate, and robust.

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