ArticleCancer causes & control : CCC2024
Pectoral muscle removal in mammogram images: A novel approach for improved accuracy and efficiency.
Article in Cancer causes & control : CCC, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of deep learning technology in breast cancer: a systematic review of segmentation, detection, and classification approaches.Biomedical engineering online · 2026Pooled it
- Deep-learning based adaptive fusion of CC and MLO views for improved mammographic cancer diagnosis.MethodsX · 2026Article
- AutoCumulus: an automated mammographic density measure created using artificial intelligence.BMC cancer · 2026Article
- Towards Automated Semantic Segmentation in Mammography Images for Enhanced Clinical Applications.Journal of imaging informatics in medicine · 2025Article
- Towards Robust Supervised Pectoral Muscle Segmentation in Mammography Images.Journal of imaging · 2024Article
- Volumetric Breast Density Estimation From Three-Dimensional Reconstructed Digital Breast Tomosynthesis Images Using Deep Learning.JCO clinical cancer informatics · 2024Article
- An Artificial Intelligence-Based Tool for Enhancing Pectoral Muscle Segmentation in Mammograms: Addressing Class Imbalance and Validation Challenges in Automated Breast Cancer Diagnosis.Diagnostics (Basel, Switzerland) · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
purposeAccurate pectoral muscle removal is critical in mammographic breast density estimation and many other computer-aided algorithms. We propose a novel approach to remove pectoral muscles form mediolateral oblique (MLO) view mammograms and compare accuracy and computational efficiency with existing method (Libra).
methodsA pectoral muscle identification pipeline was developed. The image is first binarized to enhance contrast and then the Canny algorithm was applied for edge detection. Robust interpolation is used to smooth out the pectoral muscle region. Accuracy and computational speed of pectoral muscle identification was assessed using 951 women (1,902 MLO mammograms) from the Joanne Knight Breast Health Cohort at Washington University School of Medicine.
resultsOur proposed algorithm exhibits lower mean error of 12.22% in comparison to Libra's estimated error of 20.44%. This 40% gain in accuracy was statistically significant (p < 0.001). The computational time for the proposed algorithm is 5.4 times faster when compared to Libra (5.1 s for proposed vs. 27.7 s for Libra per mammogram).
conclusionWe present a novel approach for pectoral muscle removal in mammogram images that demonstrates significant improvement in accuracy and efficiency compared to existing method. Our findings have important implications for the development of computer-aided systems and other automated tools in this field.
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