Divide and Conquer: A Large-Scale Dataset and Model for Left-Right Breast MRI Segmentation
This work addresses a critical gap in breast MRI analysis for women's health, but it is incremental as it focuses on dataset creation and a standard model.
The authors tackled the lack of publicly available breast MRI data with left-right segmentation labels by introducing a dataset of over 13,000 annotated cases and a deep-learning model for segmentation, providing a resource for women's health tool development.
We introduce the first publicly available breast MRI dataset with explicit left and right breast segmentation labels, encompassing more than 13,000 annotated cases. Alongside this dataset, we provide a robust deep-learning model trained for left-right breast segmentation. This work addresses a critical gap in breast MRI analysis and offers a valuable resource for the development of advanced tools in women's health. The dataset and trained model are publicly available at: www.github.com/MIC-DKFZ/BreastDivider