MAMA-MIA: A Large-Scale Multi-Center Breast Cancer DCE-MRI Benchmark Dataset with Expert Segmentations

Welcome to the MAMA-MIA dataset repository! This dataset contains 1,506 cases of breast cancer dynamic contrast-enhanced magnetic resonance images (DCE-MRI) with expert tumor segmentations. Below, you will find all the necessary information to download and use the dataset, as well as instructions on how to run inference using our pre-trained nnUNet model.

If you use the MAMA-MIA dataset or the pretrained model in your research, please cite our publication and the dataset publications of the images included in the dataset.

BibTex:

@article{Garrucho2024MAMAMIA,
  title={MAMA-MIA: A Large-Scale Multi-Center Breast Cancer DCE-MRI Benchmark Dataset with Expert Segmentations},
  author={Lidia Garrucho and Claire-Anne Reidel and Kaisar Kushibar and Smriti Joshi and Richard Osuala and Apostolia Tsirikoglou and Maciej Bobowicz and Javier del Riego and Alessandro Catanese and Katarzyna Gwoździewicz and Maria-Laura Cosaka and Pasant M. Abo-Elhoda and Sara W. Tantawy and Shorouq S. Sakrana and Norhan O. Shawky-Abdelfatah and Amr Muhammad Abdo-Salem and Androniki Kozana and Eugen Divjak and Gordana Ivanac and Katerina Nikiforaki and Michail E. Klontzas and Rosa García-Dosdá and Meltem Gulsun-Akpinar and Oğuz Lafcı and Ritse Mann and Carlos Martín-Isla and Fred Prior and Kostas Marias and Martijn P.A. Starmans and Fredrik Strand and Oliver Díaz and Laura Igual and Karim Lekadir},
  journal={arXiv preprint},
  year={2024},
  doi={arXiv:2406.13844},
  url={https://arxiv.org/abs/2406.13844}
}

Potential Applications

Some of the potential applications of the dataset are:

• Classification, Detection and Segmentation of Breast Cancer in MRI using Deep Learning.
• Radiomic extraction and Machine Learning.
• Segmentation Model Finetuning using the Pretrained nnUNet Weigths.
• Fine-tuning of Foundational Models.
• Treatment Response to Neoadjuvant Chemotherapy.
• Patient Survival Prediction.
• Segmentation Quality Control.
• Image Synthesis.

Repository Structure

├── README.md                     # Project overview, setup instructions, and usage details
├── requirements.txt              # List of Python package dependencies for running the project
├── nnUNet                        # Forked nnUNet GitHub repository for training the automatic segmentation model
├── notebooks                      # Jupyter Notebooks for project demos and examples
│   ├── demo_preprocessing_pipeline.ipynb   # Demonstrates common MRI preprocessing steps (e.g., bias field correction, normalization, resampling)
│   ├── display_clinical_data.ipynb         # Example on how to parse and visualize clinical data from the dataset
│   └── display_segmentations.ipynb         # Various methods for displaying MRI images and their corresponding segmentation masks
└── src                             # Python source code used in the Jupyter Notebooks
    ├── clinical_data.py           # Functions for handling and processing clinical data
    ├── preprocessing.py           # Preprocessing functions
    └── visualization.py           # Functions for visualizing images, segmentations, and metrics

Downloading the Dataset

The MAMA-MIA dataset is hosted on Synapse. You can download the dataset using the CLI or Python with the following code:

Command Line Interface (CLI)

synapse get syn60868042

Python

import synapseclient
syn = synapseclient.Synapse()
syn.login()
entity = syn.get("syn60868042")

Check Synapse documentation for more info.

Content

Expert Segmentations

Running Inference with nnUNet pre-trained model

The pre-trained vanilla nnUNet model has been trained using the 1506 full-image DCE-MRIs and the expert segmentations from the MAMA-MIA dataset.

Full-image Tumor Segmentation	Dice	IoU	HD95	MSD
nnUNet Fold 0	0.7600	0.6512	39.90	11.89
nnUNet Fold 1	0.7617	0.6500	41.25	12.10
nnUNet Fold 2	0.7749	0.6714	31.12	8.86
nnUNet Fold 3	0.7616	0.6577	36.25	11.74
nnUNet Fold 4	0.7520	0.6396	38.57	10,81
nnUNet Mean	0.7620	0.6539	37.41	11.08

The metrics correspond to Dice Coefficient, Intersection over Union (IoU), the 95 Percentile of Hausdorff Distance (HD95) and to the Mean Surface Distance (MSD). The distances are in mm. The library used to compute these metrics was seg-metrics v1.2.7.

Step 1. Clone the repository

Clone the forked repository of nnUNet.

git clone https://github.com/LidiaGarrucho/MAMA-MIA
cd MAMA-MIA/nnUNet

Step 2. Install the necessary dependencies

To run the pre-trained nnUNet model, follow the installation instructions.

Step 3. Download the pre-trained weights

The nnUNet pretrained weights can be dowloaded from Synapse. Unzip the folder inside nnUNet GitHub repository under nnUNet/nnunetv2/nnUNet_results.

Step 4. (Recommended) Preprocess your input MRI images

The recommended preprocessing steps to get optimum performance are:

• z-score normalization. For DCE-MRI, use the mean and standard deviation of all the phases (from pre to last post-contrast) to z-score the DCE-MRI sequence.
• isotropic pixel spacing. The MRIs were resampled using a uniform pixel spacing of [1,1,1].

Step 5. Run the nnUNet inference

nnUNetv2_predict -i /path/to/your/images -o /path/to/output -d 101 -c 3d_fullres

• Replace /path/to/your/images with the directory containing your input images.
• Replace /path/to/output with the directory where you want to save the output segmentations.

Note: An error might arise if your images are not in compressed NifTI format (.nii.gz).

Citation and Attribution

If you use the MAMA-MIA dataset or the pretrained model in your research, please cite our publication and the dataset publications of the images included in the dataset.

MAMA-MIA Dataset

Garrucho, L., Reidel, C.-A., Kushibar, K., Joshi, S., Osuala, R., Tsirikoglou, A., Bobowicz, M., del Riego, J., Catanese, A., Gwoździewicz, K., Cosaka, M.-L., Abo-Elhoda, P. M., Tantawy, S. W., Sakrana, S. S., Shawky-Abdelfatah, N. O., Abdo-Salem, A. M., Kozana, A., Divjak, E., Ivanac, G., Nikiforaki, K., Klontzas, M. E., García-Dosdá, R., Gulsun-Akpinar, M., Lafcı, O., Mann, R., Martín-Isla, C., Prior, F., Marias, K., Starmans, M. P. A., Strand, F., Díaz, O., Igual, L., and Lekadir, K. (2024). MAMA-MIA: A Large-Scale Multi-Center Breast Cancer DCE-MRI Benchmark Dataset with Expert Segmentations. arXiv preprint (arXiv:2406.13844).

BibTex:

@article{Garrucho2024MAMAMIA,
  title={MAMA-MIA: A Large-Scale Multi-Center Breast Cancer DCE-MRI Benchmark Dataset with Expert Segmentations},
  author={Lidia Garrucho and Claire-Anne Reidel and Kaisar Kushibar and Smriti Joshi and Richard Osuala and Apostolia Tsirikoglou and Maciej Bobowicz and Javier del Riego and Alessandro Catanese and Katarzyna Gwoździewicz and Maria-Laura Cosaka and Pasant M. Abo-Elhoda and Sara W. Tantawy and Shorouq S. Sakrana and Norhan O. Shawky-Abdelfatah and Amr Muhammad Abdo-Salem and Androniki Kozana and Eugen Divjak and Gordana Ivanac and Katerina Nikiforaki and Michail E. Klontzas and Rosa García-Dosdá and Meltem Gulsun-Akpinar and Oğuz Lafcı and Ritse Mann and Carlos Martín-Isla and Fred Prior and Kostas Marias and Martijn P.A. Starmans and Fredrik Strand and Oliver Díaz and Laura Igual and Karim Lekadir},
  journal={arXiv preprint},
  year={2024},
  doi={arXiv:2406.13844},
  url={https://arxiv.org/abs/2406.13844}
}

The MAMA-MIA dataset includes public DCE-MRI images from four different collection in the TCIA repository under the following licenses:

ISPY1 Trial (License CC BY 3.0)

David Newitt, Nola Hylton, on behalf of the I-SPY 1 Network and ACRIN 6657 Trial Team. (2016). Multi-center breast DCE-MRI data and segmentations from patients in the I-SPY 1/ACRIN 6657 trials. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.HdHpgJLK

Breast-MRI-NACT-Pilot (License CC BY 3.0)

Newitt, D., & Hylton, N. (2016). Single site breast DCE-MRI data and segmentations from patients undergoing neoadjuvant chemotherapy (Version 3) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.QHsyhJKy

ISPY2 Trial (License CC BY 4.0)

Li, W., Newitt, D. C., Gibbs, J., Wilmes, L. J., Jones, E. F., Arasu, V. A., Strand, F., Onishi, N., Nguyen, A. A.-T., Kornak, J., Joe, B. N., Price, E. R., Ojeda-Fournier, H., Eghtedari, M., Zamora, K. W., Woodard, S. A., Umphrey, H., Bernreuter, W., Nelson, M., … Hylton, N. M. (2022). I-SPY 2 Breast Dynamic Contrast Enhanced MRI Trial (ISPY2) (Version 1) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.D8Z0-9T85

Duke-Breast-Cancer-MRI (License CC BY-NC 4.0)

Saha, A., Harowicz, M. R., Grimm, L. J., Weng, J., Cain, E. H., Kim, C. E., Ghate, S. V., Walsh, R., & Mazurowski, M. A. (2021). Dynamic contrast-enhanced magnetic resonance images of breast cancer patients with tumor locations [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.e3sv-re93

Thank you for using our dataset and pretrained model! If you have any questions or issues, please feel free to open an issue in this repository.