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Slice-to-volume medical image registration: a survey

arXiv 1702.01636 ↗First posted 6 Feb 2017cs.CVcited by 21

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Abstract (arXiv)

During the last decades, the research community of medical imaging has witnessed continuous advances in image registration methods, which pushed the limits of the state-of-the-art and enabled the development of novel medical procedures. A particular type of image registration problem, known as slice-to-volume registration, played a fundamental role in areas like image guided surgeries and volumetric image reconstruction. However, to date, and despite the extensive literature available on this topic, no survey has been written to discuss this challenging problem. This paper introduces the first comprehensive survey of the literature about slice-to-volume registration, presenting a categorical study of the algorithms according to an ad-hoc taxonomy and analyzing advantages and disadvantages of every category. We draw some general conclusions from this analysis and present our perspectives on the future of the field.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Feb 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 1; 2023: 5; 2024: 6; 2025: 4; 2026: 3.

21 papers.

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  1. 2021Equivariant Filters for Efficient Tracking in 3D Imagingcited by 17
  2. 2023Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance imagescited by 15
  3. 2024Epicardium Prompt-guided Real-time Cardiac Ultrasound Frame-to-volume Registrationcited by 7
  4. 2022AFFIRM: Affinity Fusion-based Framework for Iteratively Random Motion correction of multi-slice fetal brain MRIcited by 5
  5. 2024Learning to Match 2D Keypoints Across Preoperative MR and Intraoperative Ultrasoundcited by 5
  6. 2024ModeTv2: GPU-accelerated Motion Decomposition Transformer for Pairwise Optimization in Medical Image Registrationcited by 4
  7. 2026DreamReg: Belief-Driven World Model for 2D-3D Ultrasound Registrationcited by 4
  8. 2025A 3D Cross-modal Keypoint Descriptor for MR-US Matching and Registrationcited by 3
  9. 2023On the Localization of Ultrasound Image Slices within Point Distribution Modelscited by 2
  10. 2025AVP-AP: Self-supervised Automatic View Positioning in 3D cardiac CT via Atlas Promptingcited by 2
  11. 2021Fetal MRI by robust deep generative prior reconstruction and diffeomorphic registration: application to gestational age predictioncited by 0
  12. 2023SuperMask: Generating High-resolution object masks from multi-view, unaligned low-resolution MRIscited by 0
9 more
  1. 2023Self-supervised Fetal MRI 3D Reconstruction Based on Radiation Diffusion Generation Modelcited by 0
  2. 2023SVRDA: A Web-based Dataset Annotation Tool for Slice-to-Volume Registrationcited by 0
  3. 2024A Literature Review on Fetus Brain Motion Correction in MRIcited by 0
  4. 2024A Unified Deep Learning Framework for Motion Correction in Medical Imagingcited by 0
  5. 2024AtlasSeg: Atlas Prior Guided Dual-U-Net for Cortical Segmentation in Fetal Brain MRIcited by 0
  6. 2025Reference-Free 3D Reconstruction of Brain Dissection Slabs via Learned Atlas Coordinatescited by 0
  7. 2025SUFFICIENT: A scan-specific unsupervised deep learning framework for high-resolution 3D isotropic fetal brain MRI reconstructioncited by 0
  8. 2026Point-Wise Geometry-Aware Transformer for Partial-to-Full Point Cloud Registration in Computer-Assisted Surgerycited by 0
  9. 2026SCoPE-Reg: Efficient Rigid Ultrasound Slice-to-Volume Registration via State-Space Correlation and Closed-Form Pose Estimationcited by 0