Discrete Optimisation for Group-Wise Cortical Surface Atlasing
Robinson EC, Glocker B, Rajchl M, Rueckert D (2016)
Publication Type: Conference contribution
Publication year: 2016
Publisher: IEEE Computer Society
Pages Range: 442-448
Conference Proceedings Title: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Event location: Las Vegas, NV, USA
ISBN: 9781467388504
Abstract
This paper presents a novel method for cortical surface atlasing. Group-wise registration is performed through a discrete optimisation framework that seeks to simultaneously improve pairwise correspondences between surface feature sets, whilst minimising a global cost relating to the rank of the feature matrix. It is assumed that when fully aligned, features will be highly linearly correlated, and thus have low rank. The framework is regularised through use of multi-resolution control point grids and higher-order smoothness terms, calculated by considering deformation strain for displacements of triplets of points. Accordingly the discrete framework is solved through high-order clique reduction. The framework is tested on cortical folding based alignment, using data from the Human Connectome Project. Preliminary results indicate that group-wise alignment improves folding correspondences, relative to registration between all pair-wise combinations, and registration to a global average template.
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United Kingdom (GB)How to cite
APA:
Robinson, E.C., Glocker, B., Rajchl, M., & Rueckert, D. (2016). Discrete Optimisation for Group-Wise Cortical Surface Atlasing. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (pp. 442-448). Las Vegas, NV, USA: IEEE Computer Society.
MLA:
Robinson, Emma C., et al. "Discrete Optimisation for Group-Wise Cortical Surface Atlasing." Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016, Las Vegas, NV, USA IEEE Computer Society, 2016. 442-448.
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