Automatic segmentation of brain MRIs and mapping neuroanatomy across the human lifespan

Shiva Keihaninejad; Rolf A. Heckemann; Ioannis S. Gousias; Daniel Rueckert; Paul Aljabar; Joseph V. Hajnal; Alexander Hammers

doi:10.1117/12.811429

27 March 2009 Automatic segmentation of brain MRIs and mapping neuroanatomy across the human lifespan

Shiva Keihaninejad, Rolf A. Heckemann, Ioannis S. Gousias, Daniel Rueckert, Paul Aljabar, Joseph V. Hajnal, Alexander Hammers

Author Affiliations +

Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 72591A (2009) https://doi.org/10.1117/12.811429
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

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, r_male=0.76, r_female=0.58 and, for hippocampal volume, r_male=-0.6, r_female=-0.4 (allρ<0.01).

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Shiva Keihaninejad, Rolf A. Heckemann, Ioannis S. Gousias, Daniel Rueckert, Paul Aljabar, Joseph V. Hajnal, and Alexander Hammers "Automatic segmentation of brain MRIs and mapping neuroanatomy across the human lifespan", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72591A (27 March 2009); https://doi.org/10.1117/12.811429

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