NC-Reg : Neural Cortical Maps for Rigid Registration
Ines Vati, Pierrick Bourgeat, Rodrigo Santa Cruz, Vincent Dore, Olivier Salvado, Clinton Fookes, L\'eo Lebrat

TL;DR
This paper introduces neural cortical maps as a continuous neural representation for cortical features, enabling faster and more accurate rigid registration of cortical surfaces, with potential clinical applications.
Contribution
It proposes neural cortical maps as a novel, efficient representation for cortical features and introduces NC-Reg, a new algorithm for rigid registration using these maps.
Findings
Achieves up to 30x faster runtimes than traditional methods.
Demonstrates sub-degree registration accuracy (<1°).
Serves as a robust pre-alignment strategy in clinical settings.
Abstract
We introduce neural cortical maps, a continuous and compact neural representation for cortical feature maps, as an alternative to traditional discrete structures such as grids and meshes. It can learn from meshes of arbitrary size and provide learnt features at any resolution. Neural cortical maps enable efficient optimization on the sphere and achieve runtimes up to 30 times faster than classic barycentric interpolation (for the same number of iterations). As a proof of concept, we investigate rigid registration of cortical surfaces and propose NC-Reg, a novel iterative algorithm that involves the use of neural cortical feature maps, gradient descent optimization and a simulated annealing strategy. Through ablation studies and subject-to-template experiments, our method demonstrates sub-degree accuracy ( from the global optimum), and serves as a promising robust pre-alignment…
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Taxonomy
Topics3D Shape Modeling and Analysis · Stochastic Gradient Optimization Techniques · Medical Image Segmentation Techniques
