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- W4287063417 abstract "Nonlinear inter-modality registration is often challenging due to the lack of objective functions that are good proxies for alignment. Here we propose a synthesis-by-registration method to convert this problem into an easier intra-modality task. We introduce a registration loss for weakly supervised image translation between domains that does not require perfectly aligned training data. This loss capitalises on a registration U-Net with frozen weights, to drive a synthesis CNN towards the desired translation. We complement this loss with a structure preserving constraint based on contrastive learning, which prevents blurring and content shifts due to overfitting. We apply this method to the registration of histological sections to MRI slices, a key step in 3D histology reconstruction. Results on two different public datasets show improvements over registration based on mutual information (13% reduction in landmark error) and synthesis-based algorithms such as CycleGAN (11% reduction), and are comparable to a registration CNN with label supervision. Code and data are publicly available at url{https://github.com/acasamitjana/SynthByReg}" @default.
- W4287063417 created "2022-07-25" @default.
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- W4287063417 date "2021-07-30" @default.
- W4287063417 modified "2023-10-18" @default.
- W4287063417 title "Synth-by-Reg (SbR): Contrastive learning for synthesis-based registration of paired images" @default.
- W4287063417 doi "https://doi.org/10.48550/arxiv.2107.14449" @default.
- W4287063417 hasPublicationYear "2021" @default.
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