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

Jyrki Lötjönen (Corresponding Author), Robin Wolz, Juha Koikkalainen, Lennart Thurfjell, Gunhild Waldemar, Hilkka Soininen, Daniel Rueckert

Research output: Contribution to journalArticleScientificpeer-review

338 Citations (Scopus)


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 0.880 (ADNI, hippocampus). The correlation coefficient for hippocampal volumes was 0.95 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.
Original languageEnglish
Pages (from-to)2352-2365
Issue number3
Publication statusPublished - 2010
MoE publication typeA1 Journal article-refereed


  • MRI
  • Segmentation
  • Atlases
  • Registration
  • Hippocampus


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