Learning Registration Models with Differentiable Gauss-Newton Optimisation
Mattias P Heinrich
We propose to capture large deformations in few iterations by learning a registration model with differentiable Gauss-Newton and compact CNNs that predict displacement gradients and a suitable residual function. By incorporating a sparse Laplacian regulariser, structural / semantic representations and weak label-supervision we achieve state-of-the-art performance for abdominal CT registration.
Wednesday 6th July
Poster Session 1.2 - onsite 11:00 - 12:00, virtual 15:20 - 16:20 (UTC+2)