datasets:
- AnonRes/OpenMind
license: cc-by-4.0
pipeline_tag: image-segmentation
tags:
- medical
OpenMind Benchmark 3D SSL Models (Primus)
This repository hosts pre-trained checkpoints for the Primus and ResEnc-L backbones from the OpenMind benchmark.
Papers:
- Primus: Enforcing Attention Usage for 3D Medical Image Segmentation
- An OpenMind for 3D medical vision self-supervised learning
Code: MIC-DKFZ/nnUNet
Pre-training framework: MIC-DKFZ/nnssl
Dataset: AnonRes/OpenMind
Overview
This repository hosts pre-trained checkpoints from the OpenMind benchmark, featuring the Primus architecture. Primus is a Transformer-centric segmentation architecture that leverages high-resolution tokens and advances in positional embeddings to achieve state-of-the-art results in 3D medical image segmentation.
Each model was pre-trained using a specific self-supervised learning (SSL) method on the OpenMind Dataset, a standardized collection of public brain MRI datasets.
Note: These models are intended to be used as weights for downstream fine-tuning for segmentation tasks using the nnU-Net framework.
Model Variants
We release SSL checkpoints for two backbone architectures:
- Primus-M: A transformer-based encoder introduced in the Primus paper.
- ResEnc-L: A CNN-based encoder.
Each encoder has been pre-trained using one of the following 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 |
Citation
@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 Köhler, 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 eand Köhler, Gregor and Maier-Hein, Klaus H},
journal={arXiv preprint arXiv:2412.17041},
year={2024}
}