Datasets:

Forithmus
/

MR-RATE-nvseg-ctmr

title: MR-RATE-nvseg-ctmr Dataset
license: cc-by-nc-sa-4.0
task_categories:
  - image-to-text
  - text-to-image
  - image-classification
  - question-answering
  - visual-question-answering
  - zero-shot-classification
language:
  - en
tags:
  - brain-mri
  - radiology
  - science
  - huggingscience
  - 3d-medical-imaging
  - medical
  - mr-rate
  - multimodal
  - vision-language
  - healthcare
  - diagnostic-imaging
  - computer-vision
  - foundation-model
size_categories:
  - 10K<n<100K
pretty_name: 'MR-RATE-nvseg-ctmr: Multi-Label Brain & Body Segmentations'
extra_gated_prompt: >
  ## Terms and Conditions for Using the MR-RATE Dataset

  **1. Acceptance of Terms** Accessing and using the MR-RATE dataset implies
  your agreement to these terms and conditions. If you disagree with any part,
  please refrain from using the dataset.


  **2. Permitted Use**

  - The dataset is intended solely for academic, research, and educational
  purposes.

  - Any commercial exploitation of the dataset without prior permission is
  strictly forbidden.

  - You must adhere to all relevant laws, regulations, and research ethics,
  including data privacy and protection standards.


  **3. Data Protection and Privacy**

  - Acknowledge the presence of sensitive information within the dataset and
  commit to maintaining data confidentiality.

  - Direct attempts to re-identify individuals from the dataset are prohibited.

  - Ensure compliance with data protection laws such as GDPR and HIPAA.


  **4. Attribution**

  - Cite the dataset and acknowledge the providers in any publications resulting
  from its use.

  - Claims of ownership or exclusive rights over the dataset or derivatives are
  not permitted.


  **5. Redistribution**

  - Redistribution of the dataset or any portion thereof is not allowed.

  - Sharing derived data must respect the privacy and confidentiality terms set
  forth.


  **6. Disclaimer**

  The dataset is provided "as is" without warranty of any kind, either expressed
  or implied, including but not limited to the accuracy or completeness of the
  data.


  **7. Limitation of Liability**

  Under no circumstances will the dataset providers be liable for any claims or
  damages resulting from your use of the dataset.


  **8. Access Revocation**

  Violation of these terms may result in the termination of your access to the
  dataset.


  **9. Amendments**

  The terms and conditions may be updated at any time; continued use of the
  dataset signifies acceptance of the new terms.


  **10. Governing Law**

  These terms are governed by the laws of the location of the dataset providers,
  excluding conflict of law rules.


  **Consent:**

  Accessing and using the MR-RATE dataset signifies your acknowledgment and
  agreement to these terms and conditions.
extra_gated_fields:
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  Last Name: text
  Institution: text
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  Email: text
  I have read and agree with Terms and Conditions for using the MR-RATE dataset: checkbox

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 native-space voxel-wise multi-label brain segmentations predicted for center modality MRI volumes and body segmentations predicted for all MRI volumes. 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.

Multi-Label Brain & Body Segmentations

For all T1-weighted center modality MRI volumes in native-space, voxel-wise anatomical brain segmentations are predicted using the NV-Segment-CTMR (MRI_BRAIN) model. Goal of sharing these segmentations is supporting region-of-interest brain analysis and various downstream tasks.

Additionally, for all MRI volumes in native-space, voxel-wise anatomical body segmentations are predicted using the NV-Segment-CTMR (MRI_BODY) model. Goal of sharing these segmentations is supporting various downstream tasks.

This repository provides:

Brain segmentations (in NIfTI format) — Voxel-wise anatomical segmentations predicted for center modality MRI volumes in native-space
Body segmentations (in NIfTI format) — Voxel-wise anatomical 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 Mega Hospital 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.