YongchengYAO/MAMA-MIA-Lite

About

This is a preprocessed redistribution of MAMA-MIA (Synapse syn60868042), which is released under the CC BY-NC 4.0 license.

Dataset summary: 1506 breast DCE-MRI scans with expert primary-tumour segmentation masks.

Contents of this repository:

• —Images/ — 1506 files
• —Masks/ — 1506 files

📝 Landmark annotations, visualization figures and the benchmark plan files live in 🔥MedVision🔥, where you can load the complete images and annotations from dataset configs.

Relation to the source dataset

All 1506 cases are included, but only one DCE phase per case. Each case ships a pre-contrast volume (_0000) plus several post-contrast phases (_0001, _0002, ...); the expert tumour mask is drawn on the first post-contrast phase, so only _0001 is redistributed. The other phases are not annotated and would not be measurable.

Why `-Lite`? The suffix marks this as a derived redistribution rather than a copy of the source. These are preprocessed volumes — every case has been format-converted where needed, geometry-normalised and reoriented to RAS+ — and for some sources cases or modalities are excluded as well (see the table above). Use it to reproduce MedVision, not as a substitute for the original release. See Preprocessing below for exactly what was changed.

Preprocessing

• —The first post-contrast volume (_0001) is used as the image, matching the phase the expert mask was drawn on (convention confirmed against the official MAMA-MIA/src/preprocessing.py::read_mri_phase_from_patient_id).

• —Images and masks standardized to RAS+ orientation; masks cast to uint16.

• —Image/mask pairs whose headers disagree on grid geometry are excluded.

Segmentation Labels

labels_map = {
    "1": "breast tumor"
}

Landmarks

landmarks_map = {
    "P1": "most right/anterior/superior endpoint of the major axis",
    "P2": "most left/superior/inferior endpoint of the major axis",
    "P3": "most right/anterior/superior endpoint of the minor axis",
    "P4": "most left/superior/inferior endpoint of the minor axis"
}

News

• —[25 Jul, 2026] Initial release. This dataset is integrated into 🔥MedVision🔥, where you can use these config names to load data in python:

• —MAMA-MIA_BoxSize_Task01_Axial_Test
• —MAMA-MIA_BoxSize_Task01_Axial_Train
• —MAMA-MIA_BoxSize_Task01_Coronal_Test
• —MAMA-MIA_BoxSize_Task01_Coronal_Train
• —MAMA-MIA_BoxSize_Task01_Sagittal_Test
• —MAMA-MIA_BoxSize_Task01_Sagittal_Train
• —MAMA-MIA_MaskSize_Task01_Axial_Test
• —MAMA-MIA_MaskSize_Task01_Axial_Train
• —MAMA-MIA_MaskSize_Task01_Coronal_Test
• —MAMA-MIA_MaskSize_Task01_Coronal_Train
• —MAMA-MIA_MaskSize_Task01_Sagittal_Test
• —MAMA-MIA_MaskSize_Task01_Sagittal_Train
• —MAMA-MIA_TumorLesionSize_Task01_Axial_Test
• —MAMA-MIA_TumorLesionSize_Task01_Axial_Train
• —MAMA-MIA_TumorLesionSize_Task01_Coronal_Test
• —MAMA-MIA_TumorLesionSize_Task01_Coronal_Train
• —MAMA-MIA_TumorLesionSize_Task01_Sagittal_Test
• —MAMA-MIA_TumorLesionSize_Task01_Sagittal_Train

Data Usage Agreement

By using the dataset, you agree to the terms as follow.

• —You must comply with the original CC BY-NC 4.0 license terms of the source dataset.
• —You are recommended to refer to the source of this dataset in any publication: https://huggingface.co/datasets/YongchengYAO/MAMA-MIA-Lite
• —You must cite the original publication(s):
• —https://doi.org/10.1038/s41597-025-04707-4
• —https://arxiv.org/abs/2603.01250

Official Release

For more information, please go to the official site: https://github.com/LidiaGarrucho/MAMA-MIA

Download from Huggingface

# python
from huggingface_hub import snapshot_download
snapshot_download(repo_id="YongchengYAO/MAMA-MIA-Lite", repo_type='dataset', local_dir="/your/local/folder")