Automatic delineation of sulci and improved partial volume classification for accurate 3D voxel-based cortical thickness estimation from MR

Acosta, Oscar, Bourgeat, Pierrick, Fripp, Jurgen, Bonner, Erik, Ourselin, Sebastien and Salvado, Olivier (2008). Automatic delineation of sulci and improved partial volume classification for accurate 3D voxel-based cortical thickness estimation from MR. In: Dimitris Metaxas, Leon Axel, Gabor Fichtinger and Gabor Szekely, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008: 11th International Conference: Proceedings, Part I. 11th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2008), New York, United States, (253-261). 6-10 September 2008. doi:10.1007/978-3-540-85988-8_31


Author Acosta, Oscar
Bourgeat, Pierrick
Fripp, Jurgen
Bonner, Erik
Ourselin, Sebastien
Salvado, Olivier
Title of paper Automatic delineation of sulci and improved partial volume classification for accurate 3D voxel-based cortical thickness estimation from MR
Conference name 11th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2008)
Conference location New York, United States
Conference dates 6-10 September 2008
Proceedings title Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008: 11th International Conference: Proceedings, Part I   Check publisher's open access policy
Journal name Lecture Notes in Computer Science   Check publisher's open access policy
Place of Publication Heidelberg, Germany
Publisher Springer
Publication Year 2008
Sub-type Fully published paper
DOI 10.1007/978-3-540-85988-8_31
ISBN 9783540859871
354085987X
ISSN 0302-9743
1611-3349
Editor Dimitris Metaxas
Leon Axel
Gabor Fichtinger
Gabor Szekely
Volume 5241
Issue 1
Start page 253
End page 261
Total pages 9
Language eng
Abstract/Summary Accurate cortical thickness estimation in-vivo is important for the study of many neurodegenerative diseases. When using magnetic resonance images (MRI), accuracy may be hampered by artifacts such as partial volume (PV) as the cortex spans only a few voxels. In zones of opposed sulcal banks (tight sulci) the measurement can be even more difficult. The aim of this work is to propose a voxel-based cortical thickness estimation method from MR by integrating a mechanism for correcting sulci delineation after an improved partial volume classification. First, an efficient and accurate framework was developed to enhance partial volume classification with structural information. Then, the correction of sulci delineation is performed after a homotopic thinning of a cost function image. Integrated to our voxel-based cortical thickness estimation pipeline, the overall method showed a better estimate of thickness and a high reproducibility on real data (R 2 > 0.9). A quantitative analysis on clinical data from an Alzheimer’s disease study showed significant differences between normal controls and Alzheimer’s disease patients.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

 
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