ArticleMedical physics2025
TrackRAD2025 challenge dataset: real-time tumor tracking for MRI-guided radiotherapy.
Article in Medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Real-time tumor tracking for magnetic resonance-guided radiotherapy using label-efficient foundation model adaptation.Physics and imaging in radiation oncology · 2026Article
- Automated annotation error detection and correction for manual two-dimensional cinematic magnetic resonance imaging segmentations using segment anything 2.Physics and imaging in radiation oncology · 2026Article
- Label-Free Lung MRI Segmentation via Misalignment-Aware Diffusion Translation.Journal of imaging informatics in medicine · 2026Article
- Depthwise-Dilated Convolutional Adapters for Medical Object Tracking and Segmentation Using the Segment Anything Model 2.Machine learning: science and technology · 2025Article
- Marker-Less Lung Tumor Tracking from Real-Time Color X-Ray Fluoroscopic Images Using Cross-Patient Deep Learning Model.Bioengineering (Basel, Switzerland) · 2025Article
- TrackRAD2025 challenge dataset: real-time tumor tracking for MRI-guided radiotherapy.Medical physics · 2025Article
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Authors and funding
28 authors.
Funding
Abstract
purposeMagnetic 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
methodsThe dataset consists of sagittal 2D cine MRIs (20-20543 frames per scan) 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.