DocumentCode :
2570745
Title :
Multi-class brain segmentation using atlas propagation and EM-based refinement
Author :
Ledig, Christian ; Wolz, Robin ; Aljabar, Paul ; Lötjönen, Jyrki ; Heckemann, Rolf A. ; Hammers, Alexander ; Rueckert, Daniel
Author_Institution :
Dept. of Comput., Imperial Coll. London, London, UK
fYear :
2012
fDate :
2-5 May 2012
Firstpage :
896
Lastpage :
899
Abstract :
In recent years, multi-atlas segmentation has emerged as one of the most accurate techniques for the segmentation of brain magnetic resonance (MR) images, especially when combined with intensity-based refinement techniques such as graph-cut or expectation-maximization (EM) optimization. However, most of the work so far has focused on intensity-based refinement strategies for individual anatomical structures such as the hippocampus. In this work we extend a previously proposed framework for labeling whole brain scans by incorporating a global and stationary Markov random field that ensures the consistency of the neighbourhood relations between structures with an a-priori defined model. In particular we improve the segmentation result of a locally weighted multi-atlas fusion method for 41 different structures simultaneously by applying a subsequent EM optimization step. We evaluate the proposed approach on 30 manually annotated brain MR images and observe an improvement of label overlaps to a manual reference by up to 6%. We also achieved a considerably improved group separation when the proposed segmentation framework is applied to a volumetric analysis of 404 subjects from the Alzheimer´s Disease Neuroimaging Initiative (ADNI) cohort.
Keywords :
Markov processes; biomedical MRI; brain; diseases; expectation-maximisation algorithm; image segmentation; medical image processing; neurophysiology; optimisation; Alzheimers disease neuroimaging initiative cohort; EM-based refinement; MRI; a-priori defined model; atlas propagation; brain magnetic resonance images; expectation-maximization optimization; global Markov random field; graph-cut optimization; hippocampus; intensity-based refinement techniques; locally weighted multiatlas fusion method; multiclass brain segmentation; stationary Markov random field; volumetric analysis; Diseases; Hippocampus; Image segmentation; Manuals; Neuroimaging; Probabilistic logic; Silicon; EM optimization; Markov random field; atlas-propagation; segmentation; simultaneous refinement;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on
Conference_Location :
Barcelona
ISSN :
1945-7928
Print_ISBN :
978-1-4577-1857-1
Type :
conf
DOI :
10.1109/ISBI.2012.6235693
Filename :
6235693
Link To Document :
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