Improved maximum a posteriori cortical segmentation by iterative relaxation of priors.
Thickness measurements of the cerebral cortex can aid diagnosis and provide valuable information about the temporal evolution of several diseases such as Alzheimer's, Huntington's, Schizophrenia, as well as normal ageing. The presence of deep sulci and 'collapsed gyri' (caused by the loss of tissue in patients with neurodegenerative diseases) complicates the tissue segmentation due to partial volume (PV) effects and limited resolution of MRI. We extend existing work to improve the segmentation and thickness estimation in a single framework. We model the PV effect using a maximum a posteriori approach with novel iterative modification of the prior information to enhance deep sulci and gyri delineation. We use a voxel based approach to estimate thickness using the Laplace equation within a Lagrangian-Eulerian framework leading to sub-voxel accuracy. Experiments performed on a new digital phantom and on clinical Alzheimer's disease MR images show improvements in both accuracy and robustness of the thickness measurements, as well as a reduction of errors in deep sulci and collapsed gyri.
Cardoso
Manuel Jorge
MJ
Centre for Medical Image Computing, University College London, UK.
Clarkson
Matthew J
MJ
Ridgway
Gerard R
GR
Modat
Marc
M
Fox
Nick C
NC
Ourselin
Sebastien
S
D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique