# Learning to Match 2D Keypoints Across Preoperative MR and Intraoperative Ultrasound

arXiv: https://arxiv.org/abs/2409.08169 · HTML: https://mlsanity.com/paper/2409.08169/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 12 Sep 2024.

## Builds on (most cited first, 8 of 8)

- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [LightGlue: Local Feature Matching at Light Speed](https://mlsanity.com/paper/2306.13643/) (2023)
- [Multimodal Generative Models for Scalable Weakly-Supervised Learning](https://mlsanity.com/paper/1802.05335/) (2018)
- [TILDE: A Temporally Invariant Learned DEtector](https://mlsanity.com/paper/1411.4568/) (2014)
- [Slice-to-volume medical image registration: a survey](https://mlsanity.com/paper/1702.01636/) (2017)
- [Unified Brain MR-Ultrasound Synthesis using Multi-Modal Hierarchical Representations](https://mlsanity.com/paper/2309.08747/) (2023)
- [A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation](https://mlsanity.com/paper/1803.07682/) (2018)
- [Driving Points Prediction For Abdominal Probabilistic Registration](https://mlsanity.com/paper/2208.03232/) (2022)

## Built on by (most cited first, 5 of 5)

- [A 3D Cross-modal Keypoint Descriptor for MR-US Matching and Registration](https://mlsanity.com/paper/2507.18551/) (2025)
- [A Systematic Benchmark of Intraoperative Ultrasound-to-MR Synthesis for Brain Tumour Surgery](https://mlsanity.com/paper/2606.00630/) (2026)
- [Deep Biomechanically-Guided Interpolation for Keypoint-Based Brain Shift Registration](https://mlsanity.com/paper/2508.13762/) (2025)
- [Data-driven registration and modeling of brain deformation for image-guided neurosurgery](https://mlsanity.com/paper/2602.10155/) (2026)
- [CrossFeat: Bridging Imaging Modalities in Feature Descriptor Space](https://mlsanity.com/paper/2609.00272/) (2026)

## Abstract (arXiv)

We propose in this paper a texture-invariant 2D keypoints descriptor specifically designed for matching preoperative Magnetic Resonance (MR) images with intraoperative Ultrasound (US) images. We introduce a matching-by-synthesis strategy, where intraoperative US images are synthesized from MR images accounting for multiple MR modalities and intraoperative US variability. We build our training set by enforcing keypoints localization over all images then train a patient-specific descriptor network that learns texture-invariant discriminant features in a supervised contrastive manner, leading to robust keypoints descriptors. Our experiments on real cases with ground truth show the effectiveness of the proposed approach, outperforming the state-of-the-art methods and achieving 80.35% matching precision on average.