  Current research in breast cancer Magnetic Resonance Imaging (MRI),
especially with Artificial Intelligence (AI), faces challenges due to the lack
of expert segmentations. To address this, we introduce the MAMA-MIA dataset,
comprising 1506 multi-center dynamic contrast-enhanced MRI cases with expert
segmentations of primary tumors and non-mass enhancement areas. These cases
were sourced from four publicly available collections in The Cancer Imaging
Archive (TCIA). Initially, we trained a deep learning model to automatically
segment the cases, generating preliminary segmentations that significantly
reduced expert segmentation time. Sixteen experts, averaging 9 years of
experience in breast cancer, then corrected these segmentations, resulting in
the final expert segmentations. Additionally, two radiologists conducted a
visual inspection of the automatic segmentations to support future quality
control studies. Alongside the expert segmentations, we provide 49 harmonized
demographic and clinical variables and the pretrained weights of the well-known
nnUNet architecture trained using the DCE-MRI full-images and expert
segmentations. This dataset aims to accelerate the development and benchmarking
of deep learning models and foster innovation in breast cancer diagnostics and
treatment planning.
