ArticleMedical image analysis2021
Difficulty-aware hierarchical convolutional neural networks for deformable registration of brain MR images.
Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Application of artificial intelligence in paediatric oncology imaging.Pediatric radiology · 2026Review
- Review
- Complex Large-Deformation Multimodality Image Registration Network for Image-Guided Radiotherapy of Cervical Cancer.Bioengineering (Basel, Switzerland) · 2024Article
- Stepwise Corrected Attention Registration Network for Preoperative and Follow-Up Magnetic Resonance Imaging of Glioma Patients.Bioengineering (Basel, Switzerland) · 2024Article
- Research on Artificial-Intelligence-Assisted Medicine: A Survey on Medical Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2024Review
- Deep convolutional neural network based on CT images of pulmonary nodules in the lungs of adolescent and young adult patients with osteosarcoma.Oncology letters · 2023Article
- The Successive Next Network as Augmented Regularization for Deformable Brain MR Image Registration.Sensors (Basel, Switzerland) · 2023Article
- Decoupled learning for brain image registration.Frontiers in neuroscience · 2023Article
- [Cascaded multi-level medical image registration method based on transformer].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- Deformable MR-CT image registration using an unsupervised, dual-channel network for neurosurgical guidance.Medical image analysis · 2022Article
- A review of deep learning-based three-dimensional medical image registration methods.Quantitative imaging in medicine and surgery · 2021Review
- Edge-Aware Pyramidal Deformable Network for Unsupervised Registration of Brain MR Images.Frontiers in neuroscience · 2020Article
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Authors and funding
5 authors.
Funding
Abstract
The aim of deformable brain image registration is to align anatomical structures, which can potentially vary with large and complex deformations. Anatomical structures vary in size and shape, requiring the registration algorithm to estimate deformation fields at various degrees of complexity. Here, we present a difficulty-aware model based on an attention mechanism to automatically identify hard-to-register regions, allowing better estimation of large complex deformations. The difficulty-aware model is incorporated into a cascaded neural network consisting of three sub-networks to fully leverage both global and local contextual information for effective registration. The first sub-network is trained at the image level to predict a coarse-scale deformation field, which is then used for initializing the subsequent sub-network. The next two sub-networks progressively optimize at the patch level with different resolutions to predict a fine-scale deformation field. Embedding difficulty-aware learning into the hierarchical neural network allows harder patches to be identified in the deeper sub-networks at higher resolutions for refining the deformation field. Experiments conducted on four public datasets validate that our method achieves promising registration accuracy with better preservation of topology, compared with state-of-the-art registration methods.
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