A method for automated cortical surface registration and labeling

Authors:
Anand A. Joshi;David W. Shattuck;Richard M. Leahy
Affiliations:
Signal and Image Processing Institute, University of Southern California, Los Angeles, CA;Laboratory of Neuro Imaging, University of California, Los Angeles, CA;Signal and Image Processing Institute, University of Southern California, Los Angeles, CA
Venue:
WBIR'12 Proceedings of the 5th international conference on Biomedical Image Registration
Year:
2012

Citing 4
Cited 0

On the limited memory BFGS method for large scale optimization

Mathematical Programming: Series A and B
Motion of curves constrained on surfaces using a level-set approach

Journal of Computational Physics
A Level Set Formulation of Geodesic Curvature Flow on Simplicial Surfaces

IEEE Transactions on Visualization and Computer Graphics
Cortical surface alignment using geometry driven multispectral optical flow

IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging

Quantified Score

Hi-index	0.00

Visualization

Abstract

Registration and delineation of anatomical features in MRI of the human brain play an important role in the investigation of brain development and disease. Accurate, automatic and computationally efficient cortical surface registration and delineation of surface-based landmarks, including regions of interest (ROIs) and sulcal curves (sulci), remain challenging problems due to substantial variation in the shapes of these features across populations. We present a method that performs a fast and accurate registration, labeling and sulcal delineation of brain images. The new method presented in this paper uses a multiresolution, curvature based approach to perform a registration of a subject brain surface model to a delineated atlas surface model; the atlas ROIs and sulcal curves are then mapped to the subject brain surface. A geodesic curvature flow on the cortical surface is then used to refine the locations of the sulcal curves sulci and label boundaries further, such that they follow the true sulcal fundi more closely. The flow is formulated using a level set based method on the cortical surface, which represents the curves as zero level sets. We also incorporate a curvature based weighting that drives the curves to the bottoms of the sulcal valleys in the cortical folds. Finally, we validate our new approach by comparing sets of automatically delineated sulcal curves it produced to corresponding sets of manually delineated sulcal curves. Our results indicate that the proposed method is able to find these landmarks accurately.