首个无追踪器3D超声重建挑战赛,推动低成本超声技术发展。
TUS-REC2024: A Challenge to Reconstruct 3D Freehand Ultrasound Without External Tracker
- 构建首个公开数据集与评估框架,实现无外部追踪的3D超声重建
- 43支团队参与,6支提交21个可运行方案,涵盖多种前沿方法
- 适合医学影像、医疗设备研发及算法工程师关注
无追踪器自由手超声重建旨在不依赖光学或电磁追踪系统的情况下,从2D超声图像序列重建3D体积。该方法避免了昂贵追踪设备,具有低成本、便携性优势,尤其适用于资源有限的临床场景。然而,长距离运动预测和复杂探头轨迹处理仍是难点。TUS-REC2024挑战赛首次建立了该领域的基准,提供大规模公开数据集、基线模型和严格评估框架。截至截止日期,共43支队伍注册,6支提交21个容器化有效解决方案。方法涵盖状态空间模型、循环模型、基于配准的体素优化、注意力机制及物理信息模型。本文综述领域背景与文献,介绍挑战设计与数据集,并对提交方法进行多指标对比分析,揭示当前技术进展与局限,为未来研究提供方向。所有数据与代码公开,支持持续开发与复现。挑战赛已举办于MICCAI 2024,并将于MICCAI 2025再度举行,体现长期推动力。
原文摘要 · Abstract (English)
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.
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