ArticleScientific reports2024
A publicly available deep learning model and dataset for segmentation of breast, fibroglandular tissue, and vessels in breast MRI.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Digital twins for non-invasive tumor localization in breast-conserving surgery: a technical pilot study.Breast (Edinburgh, Scotland) · 2026Article
- GuidedMorph: Two-Stage Deformable Registration for Breast MRI.IEEE journal of biomedical and health informatics · 2026Article
- Deep Learning-Based Synthetic Contrast-Enhanced Breast MRI for Monitoring Response to Neoadjuvant Therapy.Cancers · 2026Article
- Fréchet radiomic distance (FRD): A versatile metric for comparing medical imaging datasets.Medical image analysis · 2026Article
- A deep learning framework to stratify Nottingham histologic grade 2 breast tumors based on dynamic contrast-enhanced MRI.European radiology · 2026Article
- Automated calculation of background parenchymal enhancement as a biomarker of treatment responses and recurrence-free survival in breast cancer.Breast cancer research and treatment · 2026Article
- The role of AI for improved management of breast cancer: Enhanced diagnosis and health disparity mitigation.Computer methods and programs in biomedicine · 2025Review
- Multiparametric MRI Markers Associated with Breast Cancer Risk in Women with Dense Breasts.Cancers · 2025Article
- Racial differences in quantitative background parenchymal enhancement on breast magnetic resonance imaging.Cancer · 2025Article
- Breast density in MRI: an AI-based quantification and relationship to assessment in mammography.NPJ breast cancer · 2025Article
- Comparative analysis of deep learning architectures for breast region segmentation with a novel breast boundary proposal.Scientific reports · 2025Article
- Explainable attention based breast tumor segmentation using a combination of UNet, ResNet, DenseNet, and EfficientNet models.Scientific reports · 2025Article
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10 authors.
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Abstract
Breast density, or the amount of fibroglandular tissue (FGT) relative to the overall breast volume, increases the risk of developing breast cancer. Although previous studies have utilized deep learning to assess breast density, the limited public availability of data and quantitative tools hinders the development of better assessment tools. Our objective was to (1) create and share a large dataset of pixel-wise annotations according to well-defined criteria, and (2) develop, evaluate, and share an automated segmentation method for breast, FGT, and blood vessels using convolutional neural networks. We used the Duke Breast Cancer MRI dataset to randomly select 100 MRI studies and manually annotated the breast, FGT, and blood vessels for each study. Model performance was evaluated using the Dice similarity coefficient (DSC). The model achieved DSC values of 0.92 for breast, 0.86 for FGT, and 0.65 for blood vessels on the test set. The correlation between our model's predicted breast density and the manually generated masks was 0.95. The correlation between the predicted breast density and qualitative radiologist assessment was 0.75. Our automated models can accurately segment breast, FGT, and blood vessels using pre-contrast breast MRI data. The data and the models were made publicly available.
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