# TUS-REC2024: A Challenge to Reconstruct 3D Freehand Ultrasound Without External Tracker

arXiv: https://arxiv.org/abs/2506.21765 · HTML: https://mlsanity.com/paper/2506.21765/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 53 references that are in the map.
- First posted on arXiv 26 Jun 2025.

## Builds on (most cited first, 20 of 20)

- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs](https://mlsanity.com/paper/2303.00915/) (2023)
- [Making Convolutional Networks Shift-Invariant Again](https://mlsanity.com/paper/1904.11486/) (2019)
- [PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical Documents](https://mlsanity.com/paper/2303.07240/) (2023)
- [Why rankings of biomedical image analysis competitions should be interpreted with care](https://mlsanity.com/paper/1806.02051/) (2018)
- [BIAS: Transparent reporting of biomedical image analysis challenges](https://mlsanity.com/paper/1910.04071/) (2019)
- [Global-Local Attention for Emotion Recognition](https://mlsanity.com/paper/2111.04129/) (2021)
- [Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks](https://mlsanity.com/paper/1707.05392/) (2017)
- [Self Context and Shape Prior for Sensorless Freehand 3D Ultrasound Reconstruction](https://mlsanity.com/paper/2108.00274/) (2021)
- [Deep Motion Network for Freehand 3D Ultrasound Reconstruction](https://mlsanity.com/paper/2207.00177/) (2022)
- [Sensorless Freehand 3D Ultrasound Reconstruction via Deep Contextual Learning](https://mlsanity.com/paper/2006.07694/) (2020)
- [Bayesian Optimization for Machine Learning : A Practical Guidebook](https://mlsanity.com/paper/1612.04858/) (2016)
- [UltraGauss: Ultrafast Gaussian Reconstruction of 3D Ultrasound Volumes](https://mlsanity.com/paper/2505.05643/) (2025)
- [Trackerless freehand ultrasound with sequence modelling and auxiliary transformation over past and future frames](https://mlsanity.com/paper/2211.04867/) (2022)
- [Long-term Dependency for 3D Reconstruction of Freehand Ultrasound Without External Tracker](https://mlsanity.com/paper/2310.10248/) (2023)
- [RapidVol: Rapid Reconstruction of 3D Ultrasound Volumes from Sensorless 2D Scans](https://mlsanity.com/paper/2404.10766/) (2024)
- [Fine-grained Context and Multi-modal Alignment for Freehand 3D Ultrasound Reconstruction](https://mlsanity.com/paper/2407.04242/) (2024)
- [Nonrigid Reconstruction of Freehand Ultrasound without a Tracker](https://mlsanity.com/paper/2407.05767/) (2024)
- [MoNetV2: Enhanced Motion Network for Freehand 3D Ultrasound Reconstruction](https://mlsanity.com/paper/2506.15835/) (2025)
- [Transducer Adaptive Ultrasound Volume Reconstruction](https://mlsanity.com/paper/2011.08419/) (2020)

## Built on by (most cited first, 4 of 4)

- [DualTrack: Sensorless 3D Ultrasound needs Local and Global Context](https://mlsanity.com/paper/2509.09530/) (2025)
- [UltraGS: Real-Time Physically-Decoupled Gaussian Splatting for Ultrasound Novel View Synthesis](https://mlsanity.com/paper/2511.07743/) (2025)
- [UltrasODM: A Dual Stream Optical Flow Mamba Network for 3D Freehand Ultrasound Reconstruction](https://mlsanity.com/paper/2512.07756/) (2025)
- [Seeing Globally, Refining Locally: Global Visual Guidance and Local Ultrasound Cues for Robust Freehand 3-D Ultrasound Reconstruction](https://mlsanity.com/paper/2607.12398/) (2026)

## Abstract (arXiv)

Trackerless freehand ultrasound reconstruction aims to reconstruct 3D volumes from sequences of 2D ultrasound images without relying on external tracking systems. By eliminating the need for optical or electromagnetic trackers, this approach offers a low-cost, portable, and widely deployable alternative to more expensive volumetric ultrasound imaging systems, particularly valuable in resource-constrained clinical settings. However, predicting long-distance transformations and handling complex probe trajectories remain challenging. The TUS-REC2024 Challenge establishes the first benchmark for trackerless 3D freehand ultrasound reconstruction by providing a large publicly available dataset, along with a baseline model and a rigorous evaluation framework. By the submission deadline, the Challenge had attracted 43 registered teams, of which 6 teams submitted 21 valid dockerized solutions. The submitted methods span a wide range of approaches, including the state space model, the recurrent model, the registration-driven volume refinement, the attention mechanism, and the physics-informed model. This paper provides a comprehensive background introduction and literature review in the field, presents an overview of the challenge design and dataset, and offers a comparative analysis of submitted methods across multiple evaluation metrics. These analyses highlight both the progress and the current limitations of state-of-the-art approaches in this domain and provide insights for future research directions. All data and code are publicly available to facilitate ongoing development and reproducibility. As a live and evolving benchmark, it is designed to be continuously iterated and improved. The Challenge was held at MICCAI 2024 and is organised again at MICCAI 2025, reflecting its sustained commitment to advancing this field.