Validation of Automated White Matter Hyperintensity Segmentation
Author(s) -
Sean D. Smart,
Michael Firbank,
John T. O’Brien
Publication year - 2011
Publication title -
journal of aging research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.564
H-Index - 43
eISSN - 2090-2212
pISSN - 2090-2204
DOI - 10.4061/2011/391783
Subject(s) - medicine , hyperintensity , white matter , segmentation , artificial intelligence , medical physics , magnetic resonance imaging , radiology , computer science
. White matter hyperintensities (WMHs) are a common finding on MRI scans of older people and are associated with vascular disease. We compared 3 methods for automatically segmenting WMHs from MRI scans. Method . An operator manually segmented WMHs on MRI images from a 3T scanner. The scans were also segmented in a fully automated fashion by three different programmes. The voxel overlap between manual and automated segmentation was compared. Results . Between observer overlap ratio was 63%. Using our previously described in-house software, we had overlap of 62.2%. We investigated the use of a modified version of SPM segmentation; however, this was not successful, with only 14% overlap. Discussion . Using our previously reported software, we demonstrated good segmentation of WMHs in a fully automated fashion.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom