# Slice-to-volume medical image registration: a survey

arXiv: https://arxiv.org/abs/1702.01636 · HTML: https://mlsanity.com/paper/1702.01636/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2017.

## Built on by (most cited first, 21 of 21)

- [Equivariant Filters for Efficient Tracking in 3D Imaging](https://mlsanity.com/paper/2103.10255/) (2021)
- [Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images](https://mlsanity.com/paper/2307.08535/) (2023)
- [Epicardium Prompt-guided Real-time Cardiac Ultrasound Frame-to-volume Registration](https://mlsanity.com/paper/2406.14534/) (2024)
- [AFFIRM: Affinity Fusion-based Framework for Iteratively Random Motion correction of multi-slice fetal brain MRI](https://mlsanity.com/paper/2205.05851/) (2022)
- [Learning to Match 2D Keypoints Across Preoperative MR and Intraoperative Ultrasound](https://mlsanity.com/paper/2409.08169/) (2024)
- [ModeTv2: GPU-accelerated Motion Decomposition Transformer for Pairwise Optimization in Medical Image Registration](https://mlsanity.com/paper/2403.16526/) (2024)
- [DreamReg: Belief-Driven World Model for 2D-3D Ultrasound Registration](https://mlsanity.com/paper/2606.18825/) (2026)
- [A 3D Cross-modal Keypoint Descriptor for MR-US Matching and Registration](https://mlsanity.com/paper/2507.18551/) (2025)
- [On the Localization of Ultrasound Image Slices within Point Distribution Models](https://mlsanity.com/paper/2309.00372/) (2023)
- [AVP-AP: Self-supervised Automatic View Positioning in 3D cardiac CT via Atlas Prompting](https://mlsanity.com/paper/2504.05966/) (2025)
- [Fetal MRI by robust deep generative prior reconstruction and diffeomorphic registration: application to gestational age prediction](https://mlsanity.com/paper/2111.00102/) (2021)
- [SuperMask: Generating High-resolution object masks from multi-view, unaligned low-resolution MRIs](https://mlsanity.com/paper/2303.07517/) (2023)
- [Self-supervised Fetal MRI 3D Reconstruction Based on Radiation Diffusion Generation Model](https://mlsanity.com/paper/2310.10209/) (2023)
- [SVRDA: A Web-based Dataset Annotation Tool for Slice-to-Volume Registration](https://mlsanity.com/paper/2311.15536/) (2023)
- [A Literature Review on Fetus Brain Motion Correction in MRI](https://mlsanity.com/paper/2401.16782/) (2024)
- [A Unified Deep Learning Framework for Motion Correction in Medical Imaging](https://mlsanity.com/paper/2409.14204/) (2024)
- [AtlasSeg: Atlas Prior Guided Dual-U-Net for Cortical Segmentation in Fetal Brain MRI](https://mlsanity.com/paper/2411.02867/) (2024)
- [Reference-Free 3D Reconstruction of Brain Dissection Slabs via Learned Atlas Coordinates](https://mlsanity.com/paper/2503.09963/) (2025)
- [SUFFICIENT: A scan-specific unsupervised deep learning framework for high-resolution 3D isotropic fetal brain MRI reconstruction](https://mlsanity.com/paper/2505.17472/) (2025)
- [Point-Wise Geometry-Aware Transformer for Partial-to-Full Point Cloud Registration in Computer-Assisted Surgery](https://mlsanity.com/paper/2606.13488/) (2026)
- [SCoPE-Reg: Efficient Rigid Ultrasound Slice-to-Volume Registration via State-Space Correlation and Closed-Form Pose Estimation](https://mlsanity.com/paper/2608.28715/) (2026)

## 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.