Fast and robust multi-atlas segmentation of brain magnetic resonance images

We introduce an optimised pipeline for multi-atlas brain MRI segmentation. Both accuracy and speed of segmentation are considered We study different similarity measures used in non-rigid registration We show that intensity differences for intensity normalised images can be used instead of standard normalised mutual Information in registration without compromising the accuracy but leading to threefold decrease in the computation time We study and validate also different methods for atlas selection. Finally, we propose two new approaches for combining multi-atlas segmentation and intensity modelling based on segmentation using expectation maximisation (EM) and optimisation via graph cuts The segmentation pipeline is evaluated with two data cohorts. IBSR data (N = 18, six subcortial structures thalamus, caudate, putamen, pallidum, hippocampus, amygdala) and ADNI data (N= 60, hippocampus) The average similarity index between automatically and manually generated volumes was 0 849 (IBSR, SIX subcortical structures) and 0880 (ADNI. hippocampus). The correlation coefficient for hippocampal volumes was 095 with the ADNI data The computation time using a standard multicore PC computer was about 3-4 min. Our results compare favourably with other recently published results (C) 2009 Elsevier Inc. All rights reserved.