Abstract #4410

Automatic Gray Matter Segmentation of the Spinal Cord in 2D Phase-Sensitive Inversion Recovery Images

Esha Datta¹, Nico Papinutto¹, Regina Schlaeger¹, Julio Carballido-Gamio¹, Alyssa Zhu¹, and Roland G Henry¹

¹UCSF, San Francisco, CA, United States

This study demonstrates the accuracy and reliability of a new method for automatic segmentation of spinal cord gray matter in 2D PSIR images at 3T of the C2-C3 spinal cord level in healthy controls. This method deforms an initial contour, based on registration from a template, using an active contours algorithm to ultimately obtain the final gray matter segmentation. When comparing the automatic segmentations with manual segmentations in 12 subjects, the Dice coefficients ranged from .82 to .93, with an average of .88. In 8 additional subjects that were scanned twice, the percent changes ranged from 1% to 6%.

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