Partial volume estimation of brain cortex from MRI using topology-corrected segmentation

Rueda, Andrea, Acosta, Oscar, Bourgeat, Pierrick, Fripp, Jurgen, Bonner, Erik, Dowson, Nicholas, Couprie, Michel, Romero, Eduardo and Salvado, Olivier (2009). Partial volume estimation of brain cortex from MRI using topology-corrected segmentation. In: 2009 6th IEEE International Symposium on Biomedical Imaging: From Nano to Macro: Proceedings. 6th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Boston, MA, United States, (133-136). 28 June -1 July 2009. doi:10.1109/ISBI.2009.5193001


Author Rueda, Andrea
Acosta, Oscar
Bourgeat, Pierrick
Fripp, Jurgen
Bonner, Erik
Dowson, Nicholas
Couprie, Michel
Romero, Eduardo
Salvado, Olivier
Title of paper Partial volume estimation of brain cortex from MRI using topology-corrected segmentation
Conference name 6th IEEE International Symposium on Biomedical Imaging: From Nano to Macro
Conference location Boston, MA, United States
Conference dates 28 June -1 July 2009
Proceedings title 2009 6th IEEE International Symposium on Biomedical Imaging: From Nano to Macro: Proceedings   Check publisher's open access policy
Journal name 2009 Ieee International Symposium On Biomedical Imaging: From Nano to Macro, Vols 1 and 2   Check publisher's open access policy
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2009
Year available 2009
Sub-type Fully published paper
DOI 10.1109/ISBI.2009.5193001
Open Access Status Not Open Access
ISBN 9781424439324
ISSN 1945-7928
1945-8452
1945-7936
Start page 133
End page 136
Total pages 4
Language eng
Abstract/Summary In magnetic resonance imaging (MRI), accuracy of brain structures quantification may be affected by the partial volume (PV) effect. PV is due to the limited spatial resolution of MRI compared to the size of anatomical structures. When considering the cortex, measurements can be even more difficult as it spans only a few voxels. In tight sulci areas, where the two banks of the cortex are in contact, voxels may be misclassified. The aim of this work is to propose a new PV classification-estimation method which integrates a mechanism for correcting sulci delineation using topology preserving operators after a maximum a posteriori classification. Additionally, we improved the estimation of mixed voxels fractional content by adaptively estimating pure tissue intensity means. Accuracy and precision were assessed using simulated and real MR data and comparison with other existing approaches demonstrated the benefits of our method. Significant improvements in GM classification were brought by the topology correction. The root mean squared error diminished by 6.3% (p < 0.01) on simulated data. The reproducibility error decreased by 9.6% (p < 0.001) and the similarity measure (Jaccard) increased by 3.4% on real data. Furthermore, compared with manually-guided expert segmentations the similarity measure was improved by 12.0% (p < 0.001).
Keyword Brain tissue segmentation
Partial volume classification
Magnetic resonance imaging
Topology correction
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Non-UQ
Additional Notes Article # 5193001

Document type: Conference Paper
Sub-type: 2009 Ieee International Symposium On Biomedical Imaging: From Nano to Macro, Vols 1 and 2
Collections: ERA 2012 Admin Only
School of Information Technology and Electrical Engineering Publications
Centre for Advanced Imaging Publications
 
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