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Abstract #2851

Automatic Classification of Brain Tractography Data

Esha Datta 1 , Kesshi Jordan 1 , Eduardo Caverzasi 1 , and Roland Henry 1

1 University of California, San Francisco, San Francisco, California, United States

Diffusion MRI tractography if often used in pre-neurosurgical planning to map brain connections that are considered critical to motor, visual, and language function. Usually, this data is segmented manually through a time consuming process requiring a trained technician. This study explores the use of an alternative automatic classification method, which uses a training set to output a set of classified tracts from a set of streamlines. This method correctly identifies the rough volume of all tracts tested and for the left IFOF, the tracts classified by the algorithm and the tracts classified by humans were almost indistinguishable (P-value = .9021).

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