title: Performing label-fusion-based segmentation using multiple automatically generated templates.
abstract: Classically, model-based segmentation procedures match magnetic resonance imaging (MRI) volumes to an expertly labeled atlas using nonlinear registration. The accuracy of these techniques are limited due to atlas biases, misregistration, and resampling error. Multi-atlas-based approaches are used as a remedy and involve matching each subject to a number of manually labeled templates. This approach yields numerous independent segmentations that are fused using a voxel-by-voxel label-voting procedure. In this article, we demonstrate how the multi-atlas approach can be extended to work with input atlases that are unique and extremely time consuming to construct by generating a library of multiple automatically generated templates of different brains (MAGeT Brain). We demonstrate the efficacy of our method for the mouse and human using two different nonlinear registration algorithms (ANIMAL and ANTs). The input atlases consist a high-resolution mouse brain atlas and an atlas of the human basal ganglia and thalamus derived from serial histological data. MAGeT Brain segmentation improves the identification of the mouse anterior commissure (mean Dice Kappa values (κ = 0.801), but may be encountering a ceiling effect for hippocampal segmentations. Applying MAGeT Brain to human subcortical structures improves segmentation accuracy for all structures compared to regular model-based techniques (κ = 0.845, 0.752, and 0.861 for the striatum, globus pallidus, and thalamus, respectively). Experiments performed with three manually derived input templates suggest that MAGeT Brain can approach or exceed the accuracy of multi-atlas label-fusion segmentation (κ = 0.894, 0.815, and 0.895 for the striatum, globus pallidus, and thalamus, respectively).
authors.affiliation: Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Canada; Kimel Family Translational Imaging Genetics Research Laboratory, The Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, University of Toronto, Canada.
authors.forename: M Mallar
authors.initials: MM
authors.lastname: Chakravarty
authors.forename: Patrick
authors.initials: P
authors.lastname: Steadman
authors.forename: Matthijs C
authors.initials: MC
authors.lastname: van Eede
authors.forename: Rebecca D
authors.initials: RD
authors.lastname: Calcott
authors.forename: Victoria
authors.initials: V
authors.lastname: Gu
authors.forename: Philip
authors.initials: P
authors.lastname: Shaw
authors.forename: Armin
authors.initials: A
authors.lastname: Raznahan
authors.forename: D Louis
authors.initials: DL
authors.lastname: Collins
authors.forename: Jason P
authors.initials: JP
authors.lastname: Lerch
mesh_terms: D000293:Adolescent; D000465:Algorithms; D000818:Animals; D001271:Atlases as Topic; D001921:Brain / Q000033:anatomy & histology*; D002648:Child; D002675:Child, Preschool; D003287:Contrast Media; D005260:Female; D005682:Gadolinium; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D051379:Mice; D008810:Mice, Inbred C57BL / Q000033:anatomy & histology*; D017711:Nonlinear Dynamics; D016011:Normal Distribution; D015588:Observer Variation; D009929:Organ Size; D010363:Pattern Recognition, Automated; D012016:Reference Values; D015203:Reproducibility of Results
chemical_list: D003287:Contrast Media; D005682:Gadolinium