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

TL;DR

The paper introduces TUS-REC2024, a benchmark challenge for trackerless 3D freehand ultrasound reconstruction, providing datasets, baseline models, and evaluation frameworks to advance research in low-cost, portable ultrasound imaging.

Contribution

It establishes the first comprehensive benchmark for trackerless ultrasound reconstruction, including datasets, evaluation metrics, and analysis of diverse submitted methods.

Findings

Wide range of approaches with promising results

Identification of current limitations in long-distance transformation prediction

Insights into future research directions in trackerless ultrasound reconstruction

Abstract

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.