ArticleInternational journal of computer assisted radiology and surgery2019
Learning deep similarity metric for 3D MR-TRUS image registration.
Article in International journal of computer assisted radiology and surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Preoperative MRI-Guided Freehand Ultrasound Volume Reconstruction.Chemical & biomedical imaging · 2026Article
- DINO-Reg: Efficient Multimodal Image Registration With Distilled Features.IEEE transactions on medical imaging · 2025Article
- TRUSTED: The Paired 3D Transabdominal Ultrasound and CT Human Data for Kidney Segmentation and Registration Research.Scientific data · 2025Article
- A survey on deep learning in medical image registration: New technologies, uncertainty, evaluation metrics, and beyond.Medical image analysis · 2025Review
- A critical assessment of artificial intelligence in magnetic resonance imaging of cancer.Npj imaging · 2025Review
- The use of deep learning in interventional radiotherapy (brachytherapy): A review with a focus on open source and open data.Zeitschrift fur medizinische Physik · 2024Review
- Automatic GAN-based MRI volume synthesis from US volumes: a proof of concept investigation.Scientific reports · 2023Article
- Ultrasound Frame-to-Volume Registration via Deep Learning for Interventional Guidance.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2023Article
- Inter-fraction deformable image registration using unsupervised deep learning for CBCT-guided abdominal radiotherapy.Physics in medicine and biology · 2023Article
- Double-Camera Fusion System for Animal-Position Awareness in Farming Pens.Foods (Basel, Switzerland) · 2022Article
- Deformable CT image registration via a dual feasible neural network.Medical physics · 2022Article
- Cross-modal attention for multi-modal image registration.Medical image analysis · 2022Article
- Custom 3D fMRI Registration Template Construction Method Based on Time-Series Fusion.Diagnostics (Basel, Switzerland) · 2022Article
- Recent advances and clinical applications of deep learning in medical image analysis.Medical image analysis · 2022Review
- Joint synthesis and registration network for deformable MR-CBCT image registration for neurosurgical guidance.Physics in medicine and biology · 2022Article
- Biomechanical modelling of the pelvic system: improving the accuracy of the location of neoplasms in MRI-TRUS fusion prostate biopsy.BMC cancer · 2022Article
- Deformable MR-CT image registration using an unsupervised, dual-channel network for neurosurgical guidance.Medical image analysis · 2022Article
- A holistic overview of deep learning approach in medical imaging.Multimedia systems · 2022Article
- Artificial intelligence with deep learning in nuclear medicine and radiology.EJNMMI physics · 2021Review
- Real-time multimodal image registration with partial intraoperative point-set data.Medical image analysis · 2021Article
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Authors and funding
7 authors.
Funding
Abstract
purposeThe fusion of transrectal ultrasound (TRUS) and magnetic resonance (MR) images for guiding targeted prostate biopsy has significantly improved the biopsy yield of aggressive cancers. A key component of MR-TRUS fusion is image registration. However, it is very challenging to obtain a robust automatic MR-TRUS registration due to the large appearance difference between the two imaging modalities. The work presented in this paper aims to tackle this problem by addressing two challenges: (i) the definition of a suitable similarity metric and (ii) the determination of a suitable optimization strategy.
methodsThis work proposes the use of a deep convolutional neural network to learn a similarity metric for MR-TRUS registration. We also use a composite optimization strategy that explores the solution space in order to search for a suitable initialization for the second-order optimization of the learned metric. Further, a multi-pass approach is used in order to smooth the metric for optimization.
resultsThe learned similarity metric outperforms the classical mutual information and also the state-of-the-art MIND feature-based methods. The results indicate that the overall registration framework has a large capture range. The proposed deep similarity metric-based approach obtained a mean TRE of 3.86 mm (with an initial TRE of 16 mm) for this challenging problem.
conclusionA similarity metric that is learned using a deep neural network can be used to assess the quality of any given image registration and can be used in conjunction with the aforementioned optimization framework to perform automatic registration that is robust to poor initialization.
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