README.md

README.md

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+# [MICCAI 2025 WOMEN] BreastDivider: A Large-Scale Dataset for Left–Right Breast MRI Segmentation
+
+**Authors**: Maximilian Rokuss\*, Benjamin Hamm\*, Yannick Kirchhoff\*, Klaus Maier-Hein  
+\*Equal contribution
+
+---
+
+## 🧠 Introduction
+
+**Breast MRI** plays a pivotal role in breast cancer detection, diagnosis, and treatment planning. However, most existing segmentation models fail to distinguish between the **left and right breasts**, limiting their usefulness in tasks such as **unilateral classification, response evaluation, or post-mastectomy follow-up**.
+
+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 resource provides a foundation for developing anatomically aware AI models and enables large-scale pretraining for downstream breast MRI tasks.
+
+---
+
+## 📂 Dataset
+
+This repository includes a complete **13k+ MRI scan dataset** with left/right segmentation masks. We curated a diverse and comprehensive breast MRI dataset by aggregating scans from multiple publicly available sources:
+
+- [Duke-Breast-Cancer-MRI dataset](https://www.cancerimagingarchive.net/collection/duke-breast-cancer-mri/)
+- [MAMA-MIA](https://github.com/LidiaGarrucho/MAMA-MIA)
+- [Advanced-MRI-Breast-Lesions](https://www.cancerimagingarchive.net/collection/advanced-mri-breast-lesions/)
+- [EA1141](https://www.cancerimagingarchive.net/collection/ea1141/)
+
+In total, the dataset comprises **13,752 3D scans**. It includes a variety of common MRI modalities such as:
+
+- T1-weighted (T1)
+- T1 with contrast (T1+C)
+- T2-weighted (T2)
+- FLAIR
+- Diffusion-weighted imaging (DWI)
+
+The **Advanced-MRI-Breast-Lesions** dataset contains T1 DCE with multiple fat-saturated phases, delayed T1, T2, and T1 non-fat saturated sequences.  
+The **Duke dataset** includes pre-operative DCE MRI at 1.5T or 3T, with fat-saturated and non-fat saturated sequences.  
+The **EA1141 dataset**, collected across 48 clinical sites, features non-contrast and post-contrast T1-weighted, T2-weighted, and DWI sequences.  
+
+To ensure consistency and quality, we only included MRI volumes with:
+
+- At least **32 slices per axis**
+- A resolution of **≤ 3×3×3 mm**
+
+---
+
+## 📄 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}
+}
+```