Automated multi-class segmentation of digital mammograms with deep convolutional neural networks

Vincent Dong; Tristan D. Maidment; Lucas R. Borges; Katherine Hopkins; Johnny Kuo; Albert Milani; Peter A. Ringer; Susan Ng

doi:10.1117/12.2626624

13 July 2022 Automated multi-class segmentation of digital mammograms with deep convolutional neural networks

Vincent Dong, Tristan D. Maidment, Lucas R. Borges, Katherine Hopkins, Johnny Kuo, Albert Milani, Peter A. Ringer, Susan Ng

Author Affiliations +

Proceedings Volume 12286, 16th International Workshop on Breast Imaging (IWBI2022); 122860M (2022) https://doi.org/10.1117/12.2626624
Event: Sixteenth International Workshop on Breast Imaging, 2022, Leuven, Belgium

Abstract

Digital mammography (DM) and digital breast tomosynthesis, the gold standards for breast cancer screening, requires correct breast positioning to ensure accuracy. Improper positioning can result in missed cancers, or can lead to additional imaging. We propose an automated deep learning (DL) segmentation approach to perform multi-class identification of regions of interest (ROI) commonly used for identification of poor positioning in mediolateral oblique (MLO) breast views. We hypothesize that by leveraging the capabilities of DL through the use of the well-founded U-Net model architecture, multi-class DL-based segmentation approaches can accurately identify air, parenchyma, pectoralis, and nipple locations within MLO images. In this study, we employed model hyperparameter searches to determine optimal model parameters for our proposed DL architecture, including the optimal loss function configuration; our best model achieved an average Sørensen-Dice coefficient of 0.919 ± 0.061 on the held-out test set. We identified high levels of localization performance in the nipple ROI. We believe our proposed segmentation model can be a foundational step in further mammogram analysis, such as for breast positioning and localized image processing tools.

Citation Download Citation

Vincent Dong, Tristan D. Maidment, Lucas R. Borges, Katherine Hopkins, Johnny Kuo, Albert Milani, Peter A. Ringer, and Susan Ng "Automated multi-class segmentation of digital mammograms with deep convolutional neural networks", Proc. SPIE 12286, 16th International Workshop on Breast Imaging (IWBI2022), 122860M (13 July 2022); https://doi.org/10.1117/12.2626624

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