ArticleSimulation and synthesis in medical imaging : ... International Workshop, SASHIMI ..., held in conjunction with MICCAI ..., proceedings. SASHIMI (Workshop)2021
Synth-by-Reg (SbR): Contrastive learning for synthesis-based registration of paired images.
Article in Simulation and synthesis in medical imaging : ... International Workshop, SASHIMI ..., held in conjunction with MICCAI ..., proceedings. SASHIMI (Workshop), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Article
- A survey on deep learning in medical image registration: New technologies, uncertainty, evaluation metrics, and beyond.Medical image analysis · 2025Review
- Medical image registration and its application in retinal images: a review.Visual computing for industry, biomedicine, and art · 2024Review
- Automated deep learning segmentation of high-resolution 7 Tesla postmortem MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- General cross-modality registration framework for visible and infrared UAV target image registration.Scientific reports · 2023Article
- A ready-to-use machine learning tool for symmetric multi-modality registration of brain MRI.Scientific reports · 2023Article
- Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas.Medical image analysis · 2022Article
- Is image-to-image translation the panacea for multimodal image registration? A comparative study.PloS one · 2022Article
- A review of deep learning-based deformable medical image registration.Frontiers in oncology · 2022Review
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Authors and funding
3 authors.
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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 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 registration with label supervision. Code and data are publicly available at https://github.com/acasamitjana/SynthByReg.
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