# Patient-Specific Real-Time Segmentation in Trackerless Brain Ultrasound

arXiv: https://arxiv.org/abs/2405.09959 · HTML: https://mlsanity.com/paper/2405.09959/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 16 May 2024.

## Builds on (most cited first, 7 of 7)

- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [Automated brain extraction of multi-sequence MRI using artificial neural networks](https://mlsanity.com/paper/1901.11341/) (2019)
- [CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation](https://mlsanity.com/paper/2201.02831/) (2022)
- [Hough-CNN: Deep Learning for Segmentation of Deep Brain Regions in MRI and Ultrasound](https://mlsanity.com/paper/1601.07014/) (2016)
- [Unified Brain MR-Ultrasound Synthesis using Multi-Modal Hierarchical Representations](https://mlsanity.com/paper/2309.08747/) (2023)
- [Learning to Segment Brain Anatomy from 2D Ultrasound with Less Data](https://mlsanity.com/paper/1912.08364/) (2019)
- [RESECT-SEG: Open access annotations of intra-operative brain tumor ultrasound images](https://mlsanity.com/paper/2207.07494/) (2022)

## Built on by (most cited first, 7 of 7)

- [Unified Cross-Modal Medical Image Synthesis with Hierarchical Mixture of Product-of-Experts](https://mlsanity.com/paper/2410.19378/) (2024)
- [Adapting Vision Foundation Models for Real-time Ultrasound Image Segmentation](https://mlsanity.com/paper/2503.24368/) (2025)
- [A 3D Cross-modal Keypoint Descriptor for MR-US Matching and Registration](https://mlsanity.com/paper/2507.18551/) (2025)
- [The Brain Resection Multimodal Image Registration (ReMIND2Reg) 2025 Challenge](https://mlsanity.com/paper/2508.09649/) (2025)
- [A Systematic Benchmark of Intraoperative Ultrasound-to-MR Synthesis for Brain Tumour Surgery](https://mlsanity.com/paper/2606.00630/) (2026)
- [Automatic brain tumor segmentation in 2D intra-operative ultrasound images using magnetic resonance imaging tumor annotations](https://mlsanity.com/paper/2411.14017/) (2024)
- [Real-Time Brain Tumor Detection in Intraoperative Ultrasound Using YOLO11: From Model Training to Deployment in the Operating Room](https://mlsanity.com/paper/2501.15994/) (2025)

## Abstract (arXiv)

Intraoperative ultrasound (iUS) imaging has the potential to improve surgical outcomes in brain surgery. However, its interpretation is challenging, even for expert neurosurgeons. In this work, we designed the first patient-specific framework that performs brain tumor segmentation in trackerless iUS. To disambiguate ultrasound imaging and adapt to the neurosurgeon's surgical objective, a patient-specific real-time network is trained using synthetic ultrasound data generated by simulating virtual iUS sweep acquisitions in pre-operative MR data. Extensive experiments performed in real ultrasound data demonstrate the effectiveness of the proposed approach, allowing for adapting to the surgeon's definition of surgical targets and outperforming non-patient-specific models, neurosurgeon experts, and high-end tracking systems. Our code is available at: urlhttps://github.com/ReubenDo/MHVAE-Seg.