Least-Squares Fitting of Two 3-D Point Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Marching cubes: A high resolution 3D surface construction algorithm
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Dynamic 3D Models with Local and Global Deformations: Deformable Superquadrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Smoothing and matching of 3-D space curves
International Journal of Computer Vision
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast level set method for propagating interfaces
Journal of Computational Physics
Matching 3-D anatomical surfaces with non-rigid deformations using octree-splines
International Journal of Computer Vision
Introduction to Robotics: Mechanics and Control
Introduction to Robotics: Mechanics and Control
Digital Picture Processing
Rigid and Affine Registration of Smooth Surfaces using Differential Properties
ECCV '94 Proceedings of the Third European Conference-Volume II on Computer Vision - Volume II
Segmentation and Measurement of the Cortex from 3D MR Images
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Brain Shift Modeling for Use in Neurosurgery
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Proximity Constraints in Deformable Models for Cortical Surface Identification
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Measurement of Intraoperative Brain Surface Deformation Under a Craniotomy
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Registration of 3D Intraoperative MR Images of the Brain Using a Finite Element Biomechanical Model
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
Nonrigid image registration: guest editors' introduction
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Non-Rigid Multi-Modal Image Registration Using Cross-Cumulative Residual Entropy
International Journal of Computer Vision
Enhanced FEM-based modeling of brain shift deformation in Image-Guided Neurosurgery
Journal of Computational and Applied Mathematics
Nonrigid 3d brain registration using intensity/feature information
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Serial FEM/XFEM-based update of preoperative brain images using intraoperative MRI
Journal of Biomedical Imaging - Special issue on Mathematical Methods for Images and Surfaces 2011
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Image-guided surgery (IGS) is a technique for localizing anatomical structures on the basis of volumetric image data and for determining the optimal surgical path to reach these structures, by the means of a localization device, or probe, whose position is tracked over time. The usefulness of this technology hinges on the accuracy of the transformation between the image volume and the space occupied by the patient anatomy and spanned by the probe. Unfortunately, in neurosurgery this transformation can be degraded by intra-surgical brain shift, which often measures more than 10 mm and can exceed 25 mm. We propose a method for characterizing brain shift that is based on non-rigid surface registration, and can be combined with a constitutively realistic finite element approach for volumetric displacement estimation. The proposed registration method integrates in a unified framework all of the stages required to estimate the movement of the cortical surface in the operating room: model-based segmentation of the pre-operative brain surface in magnetic resonance image data, range-sensing of the cortex in the OR, range-MR rigid transformation computation, and range-based non-rigid brain motion estimation. The brain segmentation technique is an adaptation of the surface evolution model. Its convergence to the brain boundary is the result of a speed term restricted to white and grey matter voxels made explicit by a classifier, and the final result is post-processed to yield a Closest Point Map of the brain surface in MR space. In turn, this Closest Point Map is used to produce the homologous pairs required to determine a highly efficient, 2D spline-based, Iterative Closest Point (ICP) non-rigid surface registration. The baseline for computing intra-operative brain displacement, as well as the initial starting point of the non-rigid ICP registration, is determined by a very good rigid range-MR transformation, produced by a simple procedure for relating the range coordinate system to that of the probe, and ultimately to that of the MR volume.