ArticleFrontiers in neuroinformatics2019
Deep Learning Deformation Initialization for Rapid Groupwise Registration of Inhomogeneous Image Populations.
Article in Frontiers in neuroinformatics, 2019. 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.
- A groupwise multiresolution network for DCE-MRI image registration.Scientific reports · 2025Article
- Tumor aware recurrent inter-patient deformable image registration of computed tomography scans with lung cancer.Medical physics · 2025Article
- Groupwise registration of infant brain diffusion tensor images using intermediate subgroup templates.PloS one · 2025Article
- A Novel Registration Framework for Aligning Longitudinal Infant Brain Tensor Images.bioRxiv : the preprint server for biology · 2024Article
- A review of deep learning-based three-dimensional medical image registration methods.Quantitative imaging in medicine and surgery · 2021Review
- Three-dimensional mapping of neurofibrillary tangle burden in the human medial temporal lobe.Brain : a journal of neurology · 2021Article
- Atlas construction and spatial normalisation to facilitate radiation-induced late effects research in childhood cancer.Physics in medicine and biology · 2021Article
- Groupwise Non-Rigid Registration with Deep Learning: An Affordable Solution Applied to 2D Cardiac Cine MRI Reconstruction.Entropy (Basel, Switzerland) · 2020Article
- Groupwise Image Alignment via Self Quotient Images.Sensors (Basel, Switzerland) · 2020Article
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6 authors.
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Abstract
Groupwise image registration tackles biases that can potentially arise from inappropriate template selection. It typically involves simultaneous registration of a cohort of images to a common space that is not specified a priori. Existing groupwise registration methods are computationally complex and are only effective for image populations without large anatomical variations. In this paper, we propose a deep learning framework to rapidly estimate large deformations between images to significantly reduce structural variability. Specifically, we employ a multi-level graph coarsening method to agglomerate similar images into clusters, each represented by an exemplar image. We then use a deep learning framework to predict the initial deformations between images. Warping with the estimated deformations brings the images closer in the image manifold and their alignment can be further refined using conventional groupwise registration algorithms. We evaluated the effectiveness of our method in groupwise registration of MR brain images and compared it against state-of-the-art groupwise registration methods. Experimental results indicate that deformation initialization enables groupwise registration to converge significantly faster with competitive accuracy, therefore facilitates large-scale imaging studies.
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