Trackerless 3D Freehand Ultrasound Reconstruction Challenge 2025 (TUS-REC2025)
Creators: name: Li, Qi
name: Huang, Yuliang
name: Saeed, Shaheer U.
name: Barratt, Dean C.
name: Clarkson, Matthew J.
name: Vercauteren, Tom
name: Hu, Yipeng
Publisher: name: Zenodo
Year: 2025
Subjects: Trackerless
Freehand
Ultrasound
3D reconstruction
Spatial transformation estimation
MICCAI 2025 challenge

description: Reconstructing 2D Ultrasound (US) images into a 3D volume enables 3D representations of anatomy to be generated which are beneficial to a wide range of downstream tasks such as quantitative biometric measurement, multimodal registration, 3D visualisation and interventional guidance. Although substantive progress has been made recently through non-deep-learning- and deep-learning-based approaches, this application is still challenging due to 1) inherent accumulated error - frame-to-frame transformation error will be accumulated through time when reconstructing long sequence of US frames, and 2) a lack of publicly-accessible data with synchronised spatial location, often obtained from tracking devices, for benchmarking the performance and for training learning-based methods.

This field has witnessed the development of 3D US reconstruction from previous non-deep learning approaches such as speckle decorrelation (Chen et al. 1997) and linear regression (Prager et al. 2003) to current deep learning-based methods such as convolutional and Long Short-Term Memory (LSTM) neural networks (Guo et al. 2020, Mikaeili et al. 2022, Miura et al. 2021). One of the first deep learning based approach was proposed in (Prevost et al. 2018), in which the proposed CNN model is compared with speckle decorrelation. Since, various network models were adapted into this application, such as ConvLSTM (Luo et al. 2021) and transformers (Ning et al. 2022). Additional information (for instance signals from inertial measurement units) and sequential models have been investigated to improve the reconstruction performance (Prevost et al. 2018, Luo et al. 2022, Guo et al. 2022). However, none of these studies used the same dataset, there is a clear need for performance benchmarking to establish a standardized basis for comparison and evaluation. Moreover, several learning-based methods have relied on data from only 12-40 subjects, highlighting the necessity for additional open training data. After successfully hosting TUS-REC2024 at MICCAI2024, we propose TUS-REC2025, with new data and a new task.

In TUS-REC2024 we observed that reconstruction performance is dependent on scan protocol although the variability of protocols within the TUS-REC2024 dataset remained limited. We believe it is thus relevant to investigate a new scan protocol with more diverse probe movement. TUS-REC2025 presents a different scanning protocol, in addition to the previous TUS-REC2024 non-rotation-based protocols. The new scans are acquired from both left and right forearms of 85 volunteers (170 scans with about 260k frames), including more diverse probe movement such as rotating and tilting at various angles. The new data may further improve reconstruction performance owing to dense sampling of the area-to-be-reconstructed. In clinical practice, such US data acquired at oblique angles to the anatomy are common for avoiding obstruction and improve acoustic coupling. With 3D reconstruction as the challenge task, TUS-REC2025 aims to 1) benchmark the model performance on the new rotating data, and 2) validate the model generalisation ability among different scan protocols.

The proposed TUS-REC challenge aims to provide an extra set of practically interesting in vivo US data with new scanning protocol, tracked by a time-synchronised optical tracker, to provide a ground-truth for comparing different 3D reconstruction methods and more importantly to advance the discovery of new methods for trackerless freehand US reconstruction. The volunteer study of forearms is widely used in literature (Prevost et al. 2018, Luo et al. 2022) which is ethically and practically feasible and thus is the first step towards other specific, potentially more complex clinical applications. The outcome of the challenge includes 1) providing in open access the new US dataset with accurate positional information; 2) establishing the first benchmark for 3D US reconstruction for rotating scans, suitable for modern learning-based data-driven approaches.