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Abstract #3315

A Generic Supervised Learning Framework for Fast Brain Extraction

Yuan Liu1,2, 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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