Skeleton-based gyri sulci separation for improved assessment of cortical thickness

In order to improve classification of neurological diseases involving cortical thinning, this work proposes an approach for separating gyral and sulcal regions of the human cortex. Using data from magnetic resonance imaging, the skeleton of the brain's white matter was reconstructed and a geodesic distance measure was applied to separate gyri and sulci. Cortical thickness per subregion was measured for the entire cortex and for gyri and sulci individually in 21 patients with Alzheimer's disease, 10 patients with frontotemporal lobar degeneration composed of two subgroups and 13 control subjects. For discrimination using logistic regressions, which was assessed using leave-one-out cross-validation, improved results were obtained in five out of six group comparisons when cortical thickness measurements were constrained to gyral or sulcal regions.

[1]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[2]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[3]  N. Schuff,et al.  Different regional patterns of cortical thinning in Alzheimer's disease and frontotemporal dementia. , 2006, Brain : a journal of neurology.

[4]  A M Dale,et al.  Measuring the thickness of the human cerebral cortex from magnetic resonance images. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Alexandru Telea,et al.  Robust Classification and Analysis of Anatomical Surfaces Using 3D Skeletons , 2008, VCBM.

[6]  Guillermo Sapiro,et al.  Measurement of cortical thickness from MRI by minimum line integrals on soft‐classified tissue , 2009, Human brain mapping.

[7]  Matthias L. Schroeter,et al.  Combined Imaging Markers Dissociate Alzheimer's Disease and Frontotemporal Lobar Degeneration – An ALE Meta-Analysis , 2011, Front. Ag. Neurosci..

[8]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[9]  P. Scheltens,et al.  Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS–ADRDA criteria , 2007, The Lancet Neurology.

[10]  Stephen M. Smith,et al.  A global optimisation method for robust affine registration of brain images , 2001, Medical Image Anal..

[11]  R. Faber,et al.  Frontotemporal lobar degeneration: a consensus on clinical diagnostic criteria. , 1999, Neurology.

[12]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.