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

Fuente: arXiv

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Bibliographic Details
Main Authors:	Li, Qi, Saeed, Shaheer U., Huang, Yuliang, Luo, Mingyuan, Yan, Zhongnuo, Chen, Jiongquan, Yang, Xin, Ni, Dong, Winter, Nektarios, Nguyen, Phuc, Steinberger, Lucas, Haney, Caelan, Zhao, Yuan, Jiang, Mingjie, Ren, Bowen, Lee, SiYeoul, Kim, Seonho, Seo, MinKyung, Kim, MinWoo, Dou, Yimeng, Zhang, Zhiwei, Li, Yin, Varghese, Tomy, Barratt, Dean C., Clarkson, Matthew J., Vercauteren, Tom, Hu, Yipeng
Format:	Preprint
Published:	2025
Subjects:	Image and Video Processing Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
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_version_	1866911262847270912
author	Li, Qi Saeed, Shaheer U. Huang, Yuliang Luo, Mingyuan Yan, Zhongnuo Chen, Jiongquan Yang, Xin Ni, Dong Winter, Nektarios Nguyen, Phuc Steinberger, Lucas Haney, Caelan Zhao, Yuan Jiang, Mingjie Ren, Bowen Lee, SiYeoul Kim, Seonho Seo, MinKyung Kim, MinWoo Dou, Yimeng Zhang, Zhiwei Li, Yin Varghese, Tomy Barratt, Dean C. Clarkson, Matthew J. Vercauteren, Tom Hu, Yipeng
author_facet	Li, Qi Saeed, Shaheer U. Huang, Yuliang Luo, Mingyuan Yan, Zhongnuo Chen, Jiongquan Yang, Xin Ni, Dong Winter, Nektarios Nguyen, Phuc Steinberger, Lucas Haney, Caelan Zhao, Yuan Jiang, Mingjie Ren, Bowen Lee, SiYeoul Kim, Seonho Seo, MinKyung Kim, MinWoo Dou, Yimeng Zhang, Zhiwei Li, Yin Varghese, Tomy Barratt, Dean C. Clarkson, Matthew J. Vercauteren, Tom Hu, Yipeng
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2506_21765
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	TUS-REC2024: A Challenge to Reconstruct 3D Freehand Ultrasound Without External Tracker Li, Qi Saeed, Shaheer U. Huang, Yuliang Luo, Mingyuan Yan, Zhongnuo Chen, Jiongquan Yang, Xin Ni, Dong Winter, Nektarios Nguyen, Phuc Steinberger, Lucas Haney, Caelan Zhao, Yuan Jiang, Mingjie Ren, Bowen Lee, SiYeoul Kim, Seonho Seo, MinKyung Kim, MinWoo Dou, Yimeng Zhang, Zhiwei Li, Yin Varghese, Tomy Barratt, Dean C. Clarkson, Matthew J. Vercauteren, Tom Hu, Yipeng Image and Video Processing Computer Vision and Pattern Recognition 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.
title	TUS-REC2024: A Challenge to Reconstruct 3D Freehand Ultrasound Without External Tracker
topic	Image and Video Processing Computer Vision and Pattern Recognition
url	https://arxiv.org/abs/2506.21765