# TrackRAD2025 challenge dataset: Real-time tumor tracking for MRI-guided radiotherapy

arXiv: https://arxiv.org/abs/2503.19119 · HTML: https://mlsanity.com/paper/2503.19119/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 29 references that are in the map.
- First posted on arXiv 24 Mar 2025.

## Built on by (most cited first, 3 of 3)

- [Depthwise-Dilated Convolutional Adapters for Medical Object Tracking and Segmentation Using the Segment Anything Model 2](https://mlsanity.com/paper/2507.14613/) (2025)
- [Segment Anything for Video: A Comprehensive Review of Video Object Segmentation and Tracking from Past to Future](https://mlsanity.com/paper/2507.22792/) (2025)
- [Fine-tuning Segment Anything for Real-Time Tumor Tracking in Cine-MRI](https://mlsanity.com/paper/2510.25990/) (2025)

## Abstract (arXiv)

Purpose: Magnetic resonance imaging (MRI) to visualize anatomical motion is becoming increasingly important when treating cancer patients with radiotherapy. Hybrid MRI-linear accelerator (MRI-linac) systems allow real-time motion management during irradiation. This paper presents a multi-institutional real-time MRI time series dataset from different MRI-linac vendors. The dataset is designed to support developing and evaluating real-time tumor localization (tracking) algorithms for MRI-guided radiotherapy within the TrackRAD2025 challenge (https://trackrad2025.grand-challenge.org/). Acquisition and validation methods: The dataset consists of sagittal 2D cine MRIs in 585 patients from six centers (3 Dutch, 1 German, 1 Australian, and 1 Chinese). Tumors in the thorax, abdomen, and pelvis acquired on two commercially available MRI-linacs (0.35 T and 1.5 T) were included. For 108 cases, irradiation targets or tracking surrogates were manually segmented on each temporal frame. The dataset was randomly split into a public training set of 527 cases (477 unlabeled and 50 labeled) and a private testing set of 58 cases (all labeled). Data Format and Usage Notes: The data is publicly available under the TrackRAD2025 collection: https://doi.org/10.57967/hf/4539. Both the images and segmentations for each patient are available in metadata format. Potential Applications: This novel clinical dataset will enable the development and evaluation of real-time tumor localization algorithms for MRI-guided radiotherapy. By enabling more accurate motion management and adaptive treatment strategies, this dataset has the potential to advance the field of radiotherapy significantly.