U-net based approach for pectoralis muscle segmentation in digital mammography
Accurate segmentation of the breast is a fundamental step in computer-aided diagnosis (CAD) systems for mammography. In particular, several tasks, such as the classification of breast density, evaluation of correct positioning of the breast, and automatic detection and classification of suspicious l...
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Elsevier
2025-01-01
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| Series: | Computer Methods and Programs in Biomedicine Update |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666990025000357 |
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| author | Francesca Angelone Alfonso Maria Ponsiglione Roberto Grassi Francesco Amato Mario Sansone |
| author_facet | Francesca Angelone Alfonso Maria Ponsiglione Roberto Grassi Francesco Amato Mario Sansone |
| author_sort | Francesca Angelone |
| collection | DOAJ |
| description | Accurate segmentation of the breast is a fundamental step in computer-aided diagnosis (CAD) systems for mammography. In particular, several tasks, such as the classification of breast density, evaluation of correct positioning of the breast, and automatic detection and classification of suspicious lesions, preliminarily require an accurate segmentation of the pectoralis muscle. This study aims to propose an automatic breast segmentation algorithm that combines traditional methods with Deep Learning methods limited only to the border region between the muscle and the breast. This type of approach allows for reducing the risk of having good overall accuracy in multi-class classification that does not reflect adequate accuracy with respect to small classes, such as the pectoralis muscle in a mammographic image. The U-Net network was therefore implemented on patches extracted along the straight line with which the muscle-breast edge was first estimated. The predicted patches are repositioned to perform an edge refinement and obtain the total breast mask, using histogram-based thresholding to segment the background from the breast. The results show Dice values equal to 0.848 ± 0.196 and Jaccard index equal to 0.774 ± 0.227 for the single patches, and Dice values equal to 0.971 ± 0.011 and Jaccard index equal to 0.944 ± 0.022 for the entire breast segmentation. |
| format | Article |
| id | doaj-art-b8b2ddbf0ee74fdcb670a45c86bc8378 |
| institution | Kabale University |
| issn | 2666-9900 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computer Methods and Programs in Biomedicine Update |
| spelling | doaj-art-b8b2ddbf0ee74fdcb670a45c86bc83782025-08-20T04:00:32ZengElsevierComputer Methods and Programs in Biomedicine Update2666-99002025-01-01810021010.1016/j.cmpbup.2025.100210U-net based approach for pectoralis muscle segmentation in digital mammographyFrancesca Angelone0Alfonso Maria Ponsiglione1Roberto Grassi2Francesco Amato3Mario Sansone4University of Sannio - Department of Engineering, Corso Garibaldi 107, 82100 Benevento, ItalyDepartment of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy; Corresponding author.Department of Precision Medicine Division of Radiology, University of Campania ’Luigi Vanvitelli’, Naples, ItalyDepartment of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, ItalyDepartment of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, ItalyAccurate segmentation of the breast is a fundamental step in computer-aided diagnosis (CAD) systems for mammography. In particular, several tasks, such as the classification of breast density, evaluation of correct positioning of the breast, and automatic detection and classification of suspicious lesions, preliminarily require an accurate segmentation of the pectoralis muscle. This study aims to propose an automatic breast segmentation algorithm that combines traditional methods with Deep Learning methods limited only to the border region between the muscle and the breast. This type of approach allows for reducing the risk of having good overall accuracy in multi-class classification that does not reflect adequate accuracy with respect to small classes, such as the pectoralis muscle in a mammographic image. The U-Net network was therefore implemented on patches extracted along the straight line with which the muscle-breast edge was first estimated. The predicted patches are repositioned to perform an edge refinement and obtain the total breast mask, using histogram-based thresholding to segment the background from the breast. The results show Dice values equal to 0.848 ± 0.196 and Jaccard index equal to 0.774 ± 0.227 for the single patches, and Dice values equal to 0.971 ± 0.011 and Jaccard index equal to 0.944 ± 0.022 for the entire breast segmentation.http://www.sciencedirect.com/science/article/pii/S2666990025000357Automatic segmentationBreast imagingPectoralis muscleU-Net |
| spellingShingle | Francesca Angelone Alfonso Maria Ponsiglione Roberto Grassi Francesco Amato Mario Sansone U-net based approach for pectoralis muscle segmentation in digital mammography Computer Methods and Programs in Biomedicine Update Automatic segmentation Breast imaging Pectoralis muscle U-Net |
| title | U-net based approach for pectoralis muscle segmentation in digital mammography |
| title_full | U-net based approach for pectoralis muscle segmentation in digital mammography |
| title_fullStr | U-net based approach for pectoralis muscle segmentation in digital mammography |
| title_full_unstemmed | U-net based approach for pectoralis muscle segmentation in digital mammography |
| title_short | U-net based approach for pectoralis muscle segmentation in digital mammography |
| title_sort | u net based approach for pectoralis muscle segmentation in digital mammography |
| topic | Automatic segmentation Breast imaging Pectoralis muscle U-Net |
| url | http://www.sciencedirect.com/science/article/pii/S2666990025000357 |
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