ArticleAcademic radiology2019
Automatic Breast and Fibroglandular Tissue Segmentation in Breast MRI Using Deep Learning by a Fully-Convolutional Residual Neural Network U-Net.
Article in Academic radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 2 of them syntheses that pooled it.
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Who cites it
42 citing papers in PubMed, 2 syntheses or guidelines pooled it, 106 citations in OpenAlex.
- Pooled it
- Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.Academic radiology · 2019Pooled it
- The impact of pre-processing techniques on deep learning breast image segmentation.Scientific reports · 2025Article
- Impact of menopause and age on breast density and background parenchymal enhancement in dynamic contrast-enhanced magnetic resonance imaging.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Automated deep learning method for whole-breast segmentation in contrast-free quantitative MRI.BMC medical imaging · 2025Article
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- Comparative analysis of deep learning architectures for breast region segmentation with a novel breast boundary proposal.Scientific reports · 2025Article
- The top 100 most-cited articles on artificial intelligence in breast radiology: a bibliometric analysis.Insights into imaging · 2024Article
- MRI-based Deep Learning Models for Preoperative Breast Volume and Density Assessment Assisting Breast Reconstruction.Aesthetic plastic surgery · 2024Article
- Performance of an AI-powered visualization software platform for precision surgery in breast cancer patients.NPJ breast cancer · 2024Article
- Breast Cancer Detection and Analytics Using Hybrid CNN and Extreme Learning Machine.Journal of personalized medicine · 2024Article
- A publicly available deep learning model and dataset for segmentation of breast, fibroglandular tissue, and vessels in breast MRI.Scientific reports · 2024Article
- Mammography with deep learning for breast cancer detection.Frontiers in oncology · 2024Review
- Generalizable attention U-Net for segmentation of fibroglandular tissue and background parenchymal enhancement in breast DCE-MRI.Insights into imaging · 2023Article
- Artificial intelligence in breast cancer: application and future perspectives.Journal of cancer research and clinical oncology · 2023Review
- Fibroglandular tissue segmentation in breast MRI using vision transformers: a multi-institutional evaluation.Scientific reports · 2023Article
- Multi-class semantic segmentation of breast tissues from MRI images using U-Net based on Haar wavelet pooling.Scientific reports · 2023Article
- Improvement of semantic segmentation through transfer learning of multi-class regions with convolutional neural networks on supine and prone breast MRI images.Scientific reports · 2023Article
- Impact of Training Data, Ground Truth and Shape Variability in the Deep Learning-Based Semantic Segmentation of HeLa Cells Observed with Electron Microscopy.Journal of imaging · 2023Article
- The utilization of artificial intelligence applications to improve breast cancer detection and prognosis.Saudi medical journal · 2023Review
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 3 countries.
Funding
Abstract
RATIONALE AND
objectivesBreast segmentation using the U-net architecture was implemented and tested in independent validation datasets to quantify fibroglandular tissue volume in breast MRI. MATERIALS AND
methodsTwo datasets were used. The training set was MRI of 286 patients with unilateral breast cancer. The segmentation was done on the contralateral normal breasts. The ground truth for the breast and fibroglandular tissue (FGT) was obtained by using a template-based segmentation method. The U-net deep learning algorithm was implemented to analyze the training set, and the final model was obtained using 10-fold cross-validation. The independent validation set was MRI of 28 normal volunteers acquired using four different MR scanners. Dice Similarity Coefficient (DSC), voxel-based accuracy, and Pearson's correlation were used to evaluate the performance.
resultsFor the 10-fold cross-validation in the initial training set of 286 patients, the DSC range was 0.83-0.98 (mean 0.95 ± 0.02) for breast and 0.73-0.97 (mean 0.91 ± 0.03) for FGT; and the accuracy range was 0.92-0.99 (mean 0.98 ± 0.01) for breast and 0.87-0.99 (mean 0.97 ± 0.01) for FGT. For the entire 224 testing breasts of the 28 normal volunteers in the validation datasets, the mean DSC was 0.86 ± 0.05 for breast, 0.83 ± 0.06 for FGT; and the mean accuracy was 0.94 ± 0.03 for breast and 0.93 ± 0.04 for FGT. The testing results for MRI acquired using four different scanners were comparable.
conclusionDeep learning based on the U-net algorithm can achieve accurate segmentation results for the breast and FGT on MRI. It may provide a reliable and efficient method to process large number of MR images for quantitative analysis of breast density.
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