May 24, 2026 · cs.CVJ/K move · Enter open · S save
Ali Mikaeili Barzili, Behzad Moshiri, Hamid Azadegan, Mohammad-Reza A. Dehaqani
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran 1439957131, Iran · Max Planck Institute for Brain Research, 60438 Frankfurt am Main, Germany · School of Computer Engineering, Iran University of Science and Technology (IUST), Tehran 16846-13114, Iran
Deformable medical image registration aligns anatomical structures across images but remains computationally dense at 3D resolution. Spiking neural networks (SNNs) offer sparse event-driven computation, yet have not been systematically studied for deformable medical image registration. We introduce SpikeReg, a spiking U-Net for 3D brain MRI registration. SpikeReg is initialized from an analog ANN registration teacher, converted by layer-wise weight transfer and activation-percentile threshold calibration, and fine-tuned with a surrogate-gradient objective combining local cross-correlation, diffusion regularization, and spike-rate sparsity. On the OASIS Learn2Reg validation split (
19 image pairs), SpikeReg reaches Dice
0.7474±0.032, with no significant paired Dice difference from the ANN teacher (
0.7480±0.037,
p=0.67), at a
12.8% mean spike rate and a
55.5× projected arithmetic-energy reduction under an event-sparse SynOps/MAC proxy relative to the dense-ANN baseline. We additionally report two negative findings: displacement distillation from the ANN teacher hurts performance, and ANN teachers trained with a label-Dice loss fail to transfer through rate-code conversion. Together these results show that dense geometric prediction can be performed under sparse event-driven computation, opening a path toward neuromorphic medical image registration.