MR-RATE: A Vision-Language Foundation Model and Dataset for Magnetic Resonance Imaging

This is the MR-RATE-nvseg-ctmr repository, part of the MR-RATE dataset release. It contains multi-label segmentations predicted with the NV-Segment-CTMR model. For full dataset details, native-space MRI volumes, radiology reports, metadata, and data splits, please refer to the MR-RATE repository. To explore, download, and work with the dataset see Dataset Organization & Getting Started.

[2026-04-03] This repository replaces the old Forithmus/MR-RATE-vista-seg repository.

Multi-Label Segmentations

Within each study in native-space, a T1-weighted MRI volume is selected as the center modality and all other MRI volumes acquired in the same study are referred to as moving modalities. For center modality volumes, voxel-wise anatomical multi-label brain segmentations are predicted using the NV-Segment-CTMR (MRI_BRAIN) model. Additionally, for center and moving modality volumes, voxel-wise anatomical multi-label body segmentations are predicted using the NV-Segment-CTMR (MRI_BODY) model. Goal of sharing these segmentations is supporting region-of-interest analysis and various downstream tasks.

This repository provides:

• Brain segmentations (in NIfTI format): Voxel-wise anatomical multi-label brain segmentations predicted for center modality MRI volumes in native-space
• Body segmentations (in NIfTI format): Voxel-wise anatomical multi-label body segmentations predicted for all MRI volumes in native-space

Study folders are zipped to comply with Hugging Face's per-repository file count limits.

Citing Us

When using this dataset, please consider citing the following related papers:

Coming soon

Ethical Approval

This study was approved by the Clinical Research Ethics Committee at Istanbul Medipol University (E-10840098-772.02-6841, 27/10/2023). All MRI volumes, metadata, and radiology reports were fully anonymized prior to analysis to protect patient privacy.

License

We are committed to fostering innovation and collaboration in the research community. To this end, all elements of the MR-RATE dataset are released under a Creative Commons Attribution–NonCommercial–ShareAlike (CC BY-NC-SA) license.

This licensing framework ensures that our contributions can be freely used for non-commercial research purposes, while also encouraging contributions and modifications, provided that the original work is properly cited and any derivative works are shared under similar terms.

For commercial inquiries related to MR-RATE, please contact: contact@forithmus.com.

Acknowledgements

This project is conducted by Forithmus and the University of Zurich, in collaboration with NVIDIA and Istanbul Medipol University.

We are grateful to NVIDIA for their support, which made this work possible. We also sincerely thank Istanbul Medipol University for their support and for providing the data used in this project. High-performance computing resources were provided by NVIDIA and the University of Zurich ScienceCluster. We would also like to thank the following individuals from NVIDIA for their contributions to the development of MR-RATE: Marc Edgar, Daguang Xu, Dong Yang, Yucheng Tang, Can Zhao, Andriy Myronenko, and Pengfei Guo.

This collaboration represents an important step toward the long-term mission to make high-quality medical intelligence accessible worldwide.