Evaluation of shape-based normalization in the corpus callosum for white matter connectivity analysis.
Recently, concerns have been raised that the correspondences computed by volumetric registration within homogeneous structures are primarily driven by regularization priors that differ among algorithms. This paper explores the correspondence based on geometric models for one of those structures, midsagittal section of the corpus callosum (MSCC), and compared the result with registration paradigms. We use geometric model called continuous medial representation (cm-rep) to normalize anatomical structures on the basis of medial geometry, and use features derived from diffusion tensor tractography for validation. We show that shape-based normalization aligns subregions of the MSCC, defined by connectivity, more accurately than normalization based on volumetric registration. Furthermore, shape-based normalization helps increase the statistical power of group analysis in an experiment where features derived from diffusion tensor tractography are compared between two cohorts. These results suggest that cm-rep is an appropriate tool for normalizing the MSCC in white matter studies.
|Title:||Evaluation of shape-based normalization in the corpus callosum for white matter connectivity analysis.|
|Keywords:||Algorithms, Artificial Intelligence, Chromosome Disorders, Computer Simulation, Corpus Callosum, Diffusion Magnetic Resonance Imaging, Humans, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Models, Neurological, Models, Statistical, Nerve Fibers, Myelinated, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Subtraction Technique|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science
UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
Archive Staff Only