ArticleJournal of medical imaging (Bellingham, Wash.)2024
Deep learning-based breast region segmentation in raw and processed digital mammograms: generalization across views and vendors.
Article in Journal of medical imaging (Bellingham, Wash.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed, 8 citations in OpenAlex.
- Mitigating Domain Shift and Dataset Bias in Deep Learning Models for Cancer Diagnostics.Journal of imaging informatics in medicine · 2026Review
- AI performance varies considerably across mammography devices: a multi-site and multi-vendor retrospective study.Insights into imaging · 2026Article
- Development and internal validation of mammography feature-based prognostic models for distant recurrence-free survival of invasive breast cancer in a screening cohort.NPJ breast cancer · 2026Article
- Frequency-based texture analysis of non-Gaussian properties of digital breast tomosynthesis images and comparison across two vendors.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Inter-machine harmonization of multicenter echocardiographic images for improvement of left ventricular ejection fraction prediction model.Scientific reports · 2025Article
- Towards Automated Semantic Segmentation in Mammography Images for Enhanced Clinical Applications.Journal of imaging informatics in medicine · 2025Article
- Influence of AI Decision Support on Radiologists' Performance and Visual Search in Screening Mammography.Radiology · 2025Article
- Development of radiomics-based models on mammograms with mass lesions to predict prognostically relevant characteristics of invasive breast cancer in a screening cohort.British journal of cancer · 2025Article
- Breast Delineation in Full-Field Digital Mammography Using the Segment Anything Model.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
Purpose: We developed a segmentation method suited for both raw (for processing) and processed (for presentation) digital mammograms (DMs) that is designed to generalize across images acquired with systems from different vendors and across the two standard screening views. Approach: A U-Net was trained to segment mammograms into background, breast, and pectoral muscle. Eight different datasets, including two previously published public sets and six sets of DMs from as many different vendors, were used, totaling 322 screen film mammograms (SFMs) and 4251 DMs (2821 raw/processed pairs and 1430 only processed) from 1077 different women. Three experiments were done: first training on all SFM and processed images, second also including all raw images in training, and finally testing vendor generalization by leaving one dataset out at a time. Results: The model trained on SFM and processed mammograms achieved a good overall performance regardless of projection and vendor, with a mean (±std. dev.) dice score of Conclusions: The proposed segmentation method yields accurate overall segmentation results for both raw and processed mammograms independent of view and vendor. The code and model weights are made available.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.