Divide and Conquer: A Large-Scale Dataset and Model for Left-Right Breast MRI Segmentation

Fuente: arXiv
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Auteurs principaux: Rokuss, Maximilian, Hamm, Benjamin, Kirchhoff, Yannick, Maier-Hein, Klaus
Format: Preprint
Publié: 2025
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author Rokuss, Maximilian
Hamm, Benjamin
Kirchhoff, Yannick
Maier-Hein, Klaus
author_facet Rokuss, Maximilian
Hamm, Benjamin
Kirchhoff, Yannick
Maier-Hein, Klaus
contents 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
format Preprint
id arxiv_https___arxiv_org_abs_2507_13830
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Divide and Conquer: A Large-Scale Dataset and Model for Left-Right Breast MRI Segmentation
Rokuss, Maximilian
Hamm, Benjamin
Kirchhoff, Yannick
Maier-Hein, Klaus
Image and Video Processing
Computer Vision and Pattern Recognition
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
title Divide and Conquer: A Large-Scale Dataset and Model for Left-Right Breast MRI Segmentation
topic Image and Video Processing
Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2507.13830