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README.md

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-license: cc-by-nc-sa-4.0
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+# [MICCAI 2025 WOMEN] BreastDivider: A Large-Scale Dataset and Model for Left–Right Breast MRI Segmentation
+
+
+**Read the paper:**  [![arXiv](https://img.shields.io/badge/arXiv-2507.13830-B31B1B.svg)](https://arxiv.org/abs/2507.13830)
+
+> **Authors**: Maximilian Rokuss\*, Benjamin Hamm\*, Yannick Kirchhoff\*, Klaus Maier-Hein  
+> \*equal contribution
+
+---
+![BreastDivider Overview](assets/BreastDivider.png)
+
+## 🧠 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](https://huggingface.co/datasets/Bubenpo/BreastDividerDataset)\
+🐳 A prebuilt Docker image is available on [DockerHub](https://hub.docker.com/r/ykirchhoff/breastdivider)
+
+---
+
+## 🧪 Model
+
+The model is based on the [nnU-Net framework](https://github.com/MIC-DKFZ/nnUNet) and was trained on the full [BreastDivider dataset](https://huggingface.co/datasets/Bubenpo/BreastDividerDataset), 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
+
+#### 🛠️ Manual Installation
+
+ 1. Install nnU-Net following the official [installation instructions](https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/installation_instructions.md).
+ 2. Download the model using git or the huggingface_hub (c.f. [models-downloading](https://huggingface.co/docs/hub/models-downloading))
+ 3. Run prediction with `nnUNetv2_predict_from_modelfolder -i input_folder -o output_folder -m /path/to/BreastDividerModel`
+
+#### 🐳 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 \
+  /mnt/input /mnt/output
+```
+
+---
+
+## 📄 Citation
+
+If you use this dataset or model in your work, please cite:
+
+```bibtex
+@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}
+}
+```