ArticleScientific reports2022
Dual attention network for unsupervised medical image registration based on VoxelMorph.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 16 citations in OpenAlex.
- Data-efficient unsupervised deep learning deformable SPECT/CT registration framework for voxel-level radionuclide therapy dosimetry: validation using clinicalEJNMMI physics · 2026Article
- Deep learning-based cross-modal MR-CT registration for brain metastases radiotherapy with multi-scale feature refinement and brainstem guidance.Scientific reports · 2026Article
- Deep learning-based head and neck deformable image registration using spatio-temporal analysis and self attention.Physics and imaging in radiation oncology · 2026Article
- Hybrid transformer and convolution iteratively optimized pyramid network for brain large deformation image registration.Scientific reports · 2025Article
- [Brain magnetic resonance image registration based on parallel lightweight convolution and multi-scale fusion].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Target-based deep learning network surveillance of non-contrast computed tomography for small infarct core of acute ischemic stroke.Frontiers in neurology · 2024Article
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An accurate medical image registration is crucial in a variety of neuroscience and clinical studies. In this paper, we proposed a new unsupervised learning network, DAVoxelMorph to improve the accuracy of 3D deformable medical image registration. Based on the VoxelMorph model, our network presented two modifications, one is adding a dual attention architecture, specifically, we model semantic correlation on spatial and coordinate dimensions respectively, and the location attention module selectively aggregates the features of each location by weighting the features of all locations. The coordinate attention module further puts the location information into the channel attention. The other is introducing the bending penalty as regularization in the loss function to penalize the bending in the deformation field. Experimental results show that DAVoxelMorph achieved better registration performance including average Dice scores (0.714) and percentage of locations with non-positive Jacobian (0.345) compare with VoxelMorph (0.703, 0.355), CycleMorph (0.705, 0.133), ANTs SyN (0.707, 0.137) and NiftyReg (0.694, 0.549). Our model increases both model sensitivity and registration accuracy.
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