datasets:
- AnonRes/OpenMind
license: cc-by-4.0
pipeline_tag: image-segmentation
tags:
- medical
OpenMind Benchmark 3D SSL Models
This repository hosts pre-trained checkpoints from the OpenMind benchmark, including the Primus architecture.
Models from the papers:
- Primus: Enforcing Attention Usage for 3D Medical Image Segmentation
- An OpenMind for 3D medical vision self-supervised learning Pre-training codebase: MIC-DKFZ/nnssl
Downstream (segmentation) framework: TaWald/nnUNet / MIC-DKFZ/nnUNet Dataset: AnonRes/OpenMind
Overview
This repository provides self-supervised pre-trained weights for 3D medical image analysis. These models were pre-trained on the OpenMind Dataset, a large-scale collection of brain MRI data.
Primus and PrimusV2 are Transformer-centric segmentation architectures designed to maximize the effectiveness of attention mechanisms in 3D medical imaging. By moving away from heavy convolutional reliance, Primus achieves state-of-the-art results on several benchmarks.
These models are not recommended to be used as-is for feature extraction. Instead, we recommend using the downstream fine-tuning frameworks for segmentation available in the adaptation repository.
Model Variants
We release SSL checkpoints for two primary backbone architectures:
- ResEnc-L: A CNN-based encoder [a, b]
- Primus-M: A transformer-based encoder introduced in the Primus paper
Each encoder has been pre-trained using various SSL techniques:
| Method | Description |
|---|---|
| Volume Contrastive (VoCo) | Contrastive pretraining method for 3D volumes |
| VolumeFusion (VF) | Spatial volume fusion-based segmentation SSL method |
| Models Genesis (MG) | Reconstruction and denoising based pretraining method |
| Masked Autoencoders (MAE) | Default reconstruction based pretraining method |
| Spark 3D (S3D) | Sparse reconstruction based pretraining method (CNN only) |
| SimMIM | Simple masked reconstruction based pretraining method (TR only) |
| SwinUNETR SSL | Rotation, Contrastive and Reconstruction based pre-training method. |
| SimCLR | Transfer of 2D Contrastive learning baseline method to 3D |
Usage
To use these models for segmentation, please refer to the nnU-Net documentation for Primus.
pip install nnunetv2
Citation
If you use these models, please cite the following papers:
@article{wald2025primus,
title={Primus: Enforcing Attention Usage for 3D Medical Image Segmentation},
author={Wald, Tassilo and Roy, Saikat and Isensee, Fabian and Ulrich, Constantin and Ziegler, Sebastian and Trofimova, Dasha and Stock, Raphael and Baumgartner, Michael and K{\"o}hler, Gregor and Maier-Hein, Klaus},
journal={arXiv preprint arXiv:2503.01835},
year={2025}
}
@article{wald2024openmind,
title={An OpenMind for 3D medical vision self-supervised learning},
author={Wald, Tassilo and Ulrich, Constantin and Suprijadi, Jonathan and Ziegler, Sebastian and Nohel, Michal and Peretzke, Robin and K{\"{o}}hler, Gregor and Maier-Hein, Klaus H},
journal={arXiv preprint arXiv:2412.17041},
year={2024}
}