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Proceedings Paper

Automatic segmentation of brain MRIs and mapping neuroanatomy across the human lifespan
Author(s): Shiva Keihaninejad; Rolf A. Heckemann; Ioannis S. Gousias; Daniel Rueckert; Paul Aljabar; Joseph V. Hajnal; Alexander Hammers
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Paper Abstract

A robust model for the automatic segmentation of human brain images into anatomically defined regions across the human lifespan would be highly desirable, but such structural segmentations of brain MRI are challenging due to age-related changes. We have developed a new method, based on established algorithms for automatic segmentation of young adults' brains. We used prior information from 30 anatomical atlases, which had been manually segmented into 83 anatomical structures. Target MRIs came from 80 subjects (~12 individuals/decade) from 20 to 90 years, with equal numbers of men, women; data from two different scanners (1.5T, 3T), using the IXI database. Each of the adult atlases was registered to each target MR image. By using additional information from segmentation into tissue classes (GM, WM and CSF) to initialise the warping based on label consistency similarity before feeding this into the previous normalised mutual information non-rigid registration, the registration became robust enough to accommodate atrophy and ventricular enlargement with age. The final segmentation was obtained by combination of the 30 propagated atlases using decision fusion. Kernel smoothing was used for modelling the structural volume changes with aging. Example linear correlation coefficients with age were, for lateral ventricular volume, rmale=0.76, rfemale=0.58 and, for hippocampal volume, rmale=-0.6, rfemale=-0.4 (allρ<0.01).

Paper Details

Date Published: 27 March 2009
PDF: 12 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72591A (27 March 2009); doi: 10.1117/12.811429
Show Author Affiliations
Shiva Keihaninejad, Imperial College London (United Kingdom)
Rolf A. Heckemann, Imperial College London (United Kingdom)
Ioannis S. Gousias, Imperial College London (United Kingdom)
Daniel Rueckert, Imperial College London (United Kingdom)
Paul Aljabar, Imperial College London (United Kingdom)
Joseph V. Hajnal, Imperial College London (United Kingdom)
Alexander Hammers, Imperial College London (United Kingdom)

Published in SPIE Proceedings Vol. 7259:
Medical Imaging 2009: Image Processing
Josien P. W. Pluim; Benoit M. Dawant, Editor(s)

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