ykirchhoff/breastdivider repository overview

⁠[MICCAI 2025 WOMEN] BreastDivider: A Large-Scale Dataset and Model for Left–Right Breast MRI Segmentation

Read the paper:

⁠🧠 Introduction

Breast MRI plays a pivotal role in breast cancer detection, diagnosis, and treatment planning. BreastDivider addresses a critical limitation in breast MRI segmentation: the lack of distinction between the left and right breasts in most public datasets and models.

In this work, we introduce the first publicly available large-scale dataset with explicit left and right breast segmentation labels, comprising over 13,000 3D MRI scans. Accompanying this dataset is a robust nnU-Net–based segmentation model, trained specifically to identify and separate left and right breast regions in clinical MRI data. This effort provides a foundation for developing high-quality, anatomically aware tools for breast MRI analysis and offers opportunities for large-scale pretraining.

🗂 This repository contains the model only
📁 The dataset is available here⁠
🐳 A prebuilt Docker image is available on DockerHub⁠

⁠🧪 Model

The model is based on the nnU-Net framework⁠ and was trained on the full BreastDivider dataset⁠, using a custom configuration that allows both breasts to fit into a single 3D patch.

It generalizes well across a variety of MRI modalities, including:

• T1-weighted (T1)
• T1 with contrast (T1+C)
• T2-weighted (T2)
• FLAIR
• Diffusion-weighted imaging (DWI)

⁠🔧 How to Use

⁠🐳 Docker inference

You can use the prebuilt Docker container for easy deployment:
Pull the image:

docker pull ykirchhoff/breastdivider:latest

Run inference:

docker run --ipc=host --rm --gpus all \
  -v "/path/to/input/folder:/mnt/input" \
  -v "/path/to/output/folder:/mnt/output" \
  ykirchhoff/breastdivider:latest

⁠🛠️ Manual Installation

The trained model is also available on Hugging Face⁠ 🤗

1. Install nnU-Net following the official installation instructions⁠.
2. Download the model using git or the huggingface_hub (c.f. models-downloading⁠)
3. Run prediction with nnUNetv2_predict_from_modelfolder -i input_folder -o output_folder -m /path/to/BreastDividerModel

⁠📄 Citation

If you use this dataset or model in your work, please cite:

@article{rokuss2025breastdivider,
  title     = {Divide and Conquer: A Large-Scale Dataset and Model for Left–Right Breast MRI Segmentation},
  author    = {Rokuss, Maximilian and Hamm, Benjamin and Kirchhoff, Yannick and Maier-Hein, Klaus},
  journal   = {arXiv preprint arXiv:2507.13830},
  year      = {2025}
}