Fast geodesic shooting for landmark matching using CUDA
Jiancong Wang

TL;DR
This paper introduces a CUDA-based implementation of geodesic shooting for landmark matching, significantly accelerating computation and improving accuracy for large point sets in biomedical image registration.
Contribution
The authors develop a CUDA implementation that reduces computational complexity and enhances accuracy for geodesic shooting in landmark registration tasks.
Findings
Achieves nearly 100x speedup over CPU implementation
Improves numerical accuracy in landmark matching
Produces better registration results in biomedical applications
Abstract
Landmark matching via geodesic shooting is a prerequisite task for numerous registration based applications in biomedicine. Geodesic shooting has been developed as one solution approach and formulates the diffeomorphic registration as an optimal control problem under the Hamiltonian framework. In this framework, with landmark positions q0 fixed, the problem solely depends on the initial momentum p0 and evolves through time steps according to a set of constraint equations. Given an initial p0, the algorithm flows q and p forward through time steps, calculates a loss based on point-set mismatch and kinetic energy, back-propagate through time to calculate gradient on p0 and update it. In the forward and backward pass, a pair-wise kernel on landmark points K and additional intermediate terms have to be calculated and marginalized, leading to O(N2) computational complexity, N being the…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Advanced Image and Video Retrieval Techniques · Medical Image Segmentation Techniques
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
