Learning Registration Models with Differentiable Gauss-Newton OptimisationDownload PDF

22 Apr 2022, 20:24 (modified: 04 Jun 2022, 12:07)MIDL 2022 Short PapersReaders: Everyone
Keywords: Learn to optimise, medical image registration, second order descent
TL;DR: Learn to predict displacement Jacobian and residual vectors for improving second-order least-squares solution for image registration.
Abstract: 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.
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Paper Type: novel methodological ideas without extensive validation
Primary Subject Area: Image Registration
Secondary Subject Area: Meta Learning
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