Abstract #1791
Simple, Accurate, Whole-Brain White Matter Segmentation in 3 Seconds
Wenzhe Xue 1,2 , Christine M. Zwart 2 , and Joseph Ross Mitchell 2
1
Biomedical Informatics, Arizona State
University, Scottsdale, AZ, United States,
2
Radiology,
Mayo Clinic, Scottsdale, AZ, United States
Segmentation of brain white matter could aid assessment
of neurological disorders. However, this is not
routinely performed in clinical settings, due in part to
long computation times and the need for users to
carefully tune multiple algorithm parameters to achieve
acceptable results. Our goal is to develop a simple,
rapid, reliable and accurate technique to segment brain
white matter, gray matter and ventricles. We are
extending the new parallel level set algorithm proposed
by Roberts et. al.. This algorithm efficiently leverages
the massive parallelism of commodity graphical
processing units (GPUs) to achieve a 14x speed over
previous parallel algorithms. Despite this speed
advantage, the user is still required to place an
initial seed and then tune three parameters to achieve
acceptable results. The tuning process requires
expertise and increases segmentation time, variability,
and inconvenience. Here we report on efforts to
automatically select optimal values for the three
algorithm parameters when segmenting brain white matter
in T1-weighted MR exams.
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