Expectation-maximization with image-weighted Markov random fields to handle severe pathology

Pagnozzi, Alex M., Dowson, Nicholas, Bradley, Andrew P., Boyd, Roslyn N., Bourgeat, Pierrick and Rose, Stephen (2016). Expectation-maximization with image-weighted Markov random fields to handle severe pathology. In: 2015 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015. International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015, Adelaide, Australia, (). 23-25 November 2015. doi:10.1109/DICTA.2015.7371257


Author Pagnozzi, Alex M.
Dowson, Nicholas
Bradley, Andrew P.
Boyd, Roslyn N.
Bourgeat, Pierrick
Rose, Stephen
Title of paper Expectation-maximization with image-weighted Markov random fields to handle severe pathology
Conference name International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015
Conference location Adelaide, Australia
Conference dates 23-25 November 2015
Convener IEEE
Proceedings title 2015 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015
Place of Publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers Inc.
Publication Year 2016
Year available 2016
Sub-type Fully published paper
DOI 10.1109/DICTA.2015.7371257
Open Access Status Not Open Access
ISBN 9781467367950
Total pages 6
Collection year 2017
Language eng
Abstract/Summary This paper describes an automatic tissue segmentation algorithm for brain MRI of children with cerebral palsy (CP) who exhibit severe cortical malformations. Many of the currently popular brain segmentation techniques rely on registered atlas priors and so generalize poorly to severely injured data sets, because of large discrepancies between the target brain and healthy (or injured) atlases. We propose a prior-less approach combined with a modification of the Expectation Maximization (EM)/Markov Random Field (MRF) segmentation by imposing a continuous weighting scheme to penalize intensity discrepancies between pairs of neighbors within each clique neighborhood, to provide robustness to the unique clinical problem of severe anatomical distortion. This approach was applied to gray matter segmentations in 20 3D T1-weighted MRIs, of which 17 were of CP patients exhibiting severe malformation. We compare our adaptive algorithm to the popular 'FreeSurfer', 'NiftySeg', 'FAST' and 'Atropos' segmentations, which collectively are state-of-The-Art surface deformation and EM approaches. The algorithm driven approach yielded improved segmentations (DSC 0.66 v 0.44 (FreeSurfer) v 0.60 (NiftySeg with 100% atlas prior relaxation) v 0.59 (FAST) v 0.64 (Atropos)) of the cerebral cortex relative to several ground-Truth manual segmentations, when compared to the existing approaches.
Keyword Cerebral palsy
Expectation maximization
Magnetic resonance imaging
Markov random field
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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