Automated segmentation of ventricles from serial brain mri for the quantification of volumetric changes associated with communicating hydrocephalus in patients with brain tumor

Published

Conference Paper

Accurate ventricle volume estimates could improve the understanding and diagnosis of postoperative communicating hydrocephalus. For this category of patients, associated changes in ventricle volume can be difficult to identify, particularly over short time intervals. We present an automated segmentation algorithm that evaluates ventricle size from serial brain MRI examination. The technique combines serial T1-weighted images to increase SNR and segments the means image to generate a ventricle template. After pre-processing, the segmentation is initiated by a fuzzy c-means clustering algorithm to find the seeds used in a combination of fast marching methods and geodesic active contours. Finally, the ventricle template is propagated onto the serial data via non-linear registration. Serial volume estimates were obtained in an automated robust and accurate manner from difficult data.© 2011 SPIE.

Full Text

Duke Authors

Cited Authors

  • Pura, JA; Hamilton, AM; Vargish, GA; Butman, JA; Linguraru, MG

Published Date

  • May 16, 2011

Published In

Volume / Issue

  • 7965 /

International Standard Serial Number (ISSN)

  • 1605-7422

International Standard Book Number 13 (ISBN-13)

  • 9780819485076

Digital Object Identifier (DOI)

  • 10.1117/12.877679

Citation Source

  • Scopus