liamchalcroft/FOMO300K-jvol

--- license: cc-by-nc-sa-4.0 tags: - brain - mri - ssl - foundation_model - 3d - image - jvol - compressed pretty_name: FOMO-300K (jvol-compressed) size_categories: - 100K<n<1M task_categories: - image-feature-extraction - zero-shot-classification viewer: false --- # FOMO-300K (jvol-compressed) This is a **wavelet-compressed version** of the [FOMO-300K](https://huggingface.co/datasets/FOMO-MRI/FOMO300K) brain MRI dataset, using the [jvol](https://github.com/fepegar/jvol-rust) compression format. All NIfTI files (`.nii.gz`) have been compressed to `.jvol` format using **quality 60** (lossy), achieving approximately **4.1x compression** over the original gzipped NIfTI data. ## What is jvol? [jvol](https://github.com/fepegar/jvol-rust) is a wavelet-based JPEG compression format for 3D medical images. It supports both lossy and lossless compression and can achieve significant compression ratios while preserving image quality suitable for most research and training applications. Install jvol: ```bash pip install jvol ``` ### Reading jvol files in Python ```python import jvol # Load a jvol file as a nibabel-compatible image img = jvol.read("path/to/scan.jvol") # Access the data as a numpy array data = img.get_fdata() # Access the affine matrix affine = img.affine ``` ### Converting back to NIfTI ```bash # Command line jvol decompress scan.jvol scan.nii.gz # Or in Python import jvol import nibabel as nib img = jvol.read("scan.jvol") nib.save(img, "scan.nii.gz") ``` ## Compression details | | Original (FOMO-300K) | Compressed (jvol q=60) | |---|---|---| | **Total size** | ~2.3 TB | ~560 GB | | **Compression ratio** | — | ~4.1x | | **Format** | `.nii.gz` | `.jvol` | | **Quality setting** | — | 60 (lossy) | ## Dataset structure The directory structure mirrors the original FOMO-300K layout, with `.nii.gz` files replaced by `.jvol`: ``` PT001_ClevelandCCF/ sub-01/ ses-01/ t1w.jvol PT002_Nigerian_Clinical/ sub-01/ ses-01/ t1w.jvol ... ``` The metadata files (`participants.tsv`, `mapping.tsv`, `mri_info.tsv`) are included unchanged from the original dataset. ## Original dataset This is a derived version of **FOMO-300K**, a large-scale dataset of brain MRI scans containing: - 59,969 subjects - 82,678 sessions - 318,877 scans across 37 dataset collections, including both clinical and research-grade scans with a wide range of sequences (T1, MPRAGE, T2, T2*, FLAIR, SWI, T1c, PD, DWI, ADC, and more). For full details about the original dataset, see: - **Dataset**: [FOMO-MRI/FOMO300K](https://huggingface.co/datasets/FOMO-MRI/FOMO300K) - **Paper**: [A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning](https://arxiv.org/abs/2506.14432) ## License & attribution This compressed version is distributed under the same [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license as the original FOMO-300K dataset. All original dataset licenses, data use agreements, and citation requirements apply. Please refer to the [original dataset page](https://huggingface.co/datasets/FOMO-MRI/FOMO300K) for full terms. ## Citation If you use this dataset, please cite the original FOMO-300K paper: ```bibtex @article{Cerri2026large, title={A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning}, author={Cerri, Stefano and Munk, Asbj{\o}rn and Llambias, Sebastian N{\o}rgaard and Ambsdorf, Jakob and Machnio, Julia and Nersesjan, Vardan and Hedeager Krag, Christian and Liu, Peirong and Rocamora Garc{\'\i}a, Pablo and Mehdipour Ghazi, Mostafa and Boesen, Mikael and Benros, Michael Eriksen and Iglesias, Juan Eugenio and Nielsen, Mads}, journal={arXiv preprint arXiv:2506.14432}, year={2026}, url={https://arxiv.org/abs/2506.14432} } ``` And cite the jvol compression tool: ```bibtex @software{jvol, author={P\'{e}rez-Garc\'{i}a, Fernando}, title={jvol: Wavelet-based compression for 3D medical images}, url={https://github.com/fepegar/jvol-rust} } ```license: CC BY-NC-SA 4.0 tags: - 脑部 - 磁共振成像（MRI） - 自监督学习（SSL） - 基础模型 - 三维 - 图像 - jvol - 压缩 pretty_name: FOMO-300K（jvol压缩版） size_categories: - 10万<数据量<100万 task_categories: - 图像特征提取 - 零样本分类 viewer: false --- # FOMO-300K（jvol压缩版） 本数据集为[FOMO-300K](https://huggingface.co/datasets/FOMO-MRI/FOMO300K)脑部磁共振成像（MRI）数据集的小波压缩版本，采用[jvol](https://github.com/fepegar/jvol-rust)压缩格式。 所有NIfTI文件（`.nii.gz`格式）均以**质量等级60**（有损压缩）被压缩至`.jvol`格式，相较于原始gzip压缩的NIfTI数据，压缩比约为**4.1倍**。 ## 什么是jvol？ [jvol](https://github.com/fepegar/jvol-rust)是一种面向三维医学图像的基于小波变换的类JPEG压缩格式，支持有损与无损压缩，可在保持适配多数研究与训练场景的图像质量的同时，实现可观的压缩比。 ### 安装jvol bash pip install jvol ### Python读取jvol文件 python import jvol # 将jvol文件加载为兼容nibabel的图像对象 img = jvol.read("path/to/scan.jvol") # 以NumPy数组形式获取图像数据 data = img.get_fdata() # 获取仿射矩阵 affine = img.affine ### 转换回NIfTI格式 bash # 命令行方式 jvol decompress scan.jvol scan.nii.gz # 或通过Python实现 import jvol import nibabel as nib img = jvol.read("scan.jvol") nib.save(img, "scan.nii.gz") ## 压缩详情 | | 原始数据集（FOMO-300K） | 压缩后数据集（jvol q=60） | |---|---|---| | **总容量** | 约2.3 TB | 约560 GB | | **压缩比** | — | 约4.1倍 | | **格式** | `.nii.gz` | `.jvol` | | **质量设置** | — | 60（有损压缩） | ## 数据集结构 目录结构与原始FOMO-300K数据集保持一致，仅将`.nii.gz`文件替换为`.jvol`格式： PT001_ClevelandCCF/ sub-01/ ses-01/ t1w.jvol PT002_Nigerian_Clinical/ sub-01/ ses-01/ t1w.jvol ... 元数据文件（`participants.tsv`、`mapping.tsv`、`mri_info.tsv`）与原始数据集完全一致，未做修改。 ## 原始数据集 本数据集是**FOMO-300K**的衍生版本。FOMO-300K是一个大规模脑部MRI扫描数据集，包含： - 59969名受试者 - 82678次扫描会话 - 318877次扫描 涵盖37个数据集集合，包含临床级与研究级扫描图像，支持多种扫描序列（T1、MPRAGE、T2、T2*、FLAIR、SWI、T1c、PD、DWI、ADC等）。 如需了解原始数据集的完整详情，请参考： - **数据集页面**：[FOMO-MRI/FOMO300K](https://huggingface.co/datasets/FOMO-MRI/FOMO300K) - **相关论文**：[面向自监督学习的大规模异构三维脑部磁共振成像数据集](https://arxiv.org/abs/2506.14432) ## 许可与署名 本压缩版数据集与原始FOMO-300K数据集采用相同的[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)许可协议。所有原始数据集的许可协议、数据使用协议与引用要求均适用。完整条款请参阅[原始数据集页面](https://huggingface.co/datasets/FOMO-MRI/FOMO300K)。 ## 引用 若您使用本数据集，请引用原始FOMO-300K论文： bibtex @article{Cerri2026large, title={面向自监督学习的大规模异构三维脑部磁共振成像数据集}, author={Cerri, Stefano and Munk, Asbjørn and Llambias, Sebastian Nørgaard and Ambsdorf, Jakob and Machnio, Julia and Nersesjan, Vardan and Hedeager Krag, Christian and Liu, Peirong and Rocamora García, Pablo and Mehdipour Ghazi, Mostafa and Boesen, Mikael and Benros, Michael Eriksen and Iglesias, Juan Eugenio and Nielsen, Mads}, journal={arXiv预印本 arXiv:2506.14432}, year={2026}, url={https://arxiv.org/abs/2506.14432} } 同时请引用jvol压缩工具： bibtex @software{jvol, author={Pérez-García, Fernando}, title={jvol：面向三维医学图像的小波压缩工具}, url={https://github.com/fepegar/jvol-rust} }