In Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE : In mammography, breast compression is achieved by lowering a compression paddle on the breast. Despite the directive that compression is needed, there is no concrete guideline on its execution. To estimate the degree of compression, current mammography units only provide compression force and breast thickness as parameters. Therefore, radiographers could be induced to mainly determine the level of compression based on compression force and apply the same value to all breast sizes. In this case, smaller breast sizes are exposed to higher pressure. This results in a highly varying perception of discomfort or even pain during the procedure, depending on the breast size.
METHODS : To overcome this imbalance, current research results suggest that pressure might be a more qualified parameter for a more uniform compression among all breast sizes. To utilize pressure, the contact area between breast and compression paddle must be determined. In this paper, we present an easy-to-implement prototype enabling a real-time pressure-based measure without the need of direct patient contact. Using an optical camera, the contact area between the breast and the compression paddle is automatically segmented by a deep learning model.
RESULTS : The model provides a mean pixel accuracy of 96.7% (SD: 2.3%), mean frequency-weighted intersection over union of 88.5% (SD: 6.3%), and a Dice score of 93.6% (SD: 2.2%). The subsequent pressure display is updated more than five times per second which enables the use in clinical routines to set the compression level.
CONCLUSION : This prototype could help guiding to an improved breast compression routine in mammography procedures.
Hertel Madeleine, Liu Chang, Song Haobo, Golatta Michael, Kappler Steffen, Nanke Ralf, Radicke Marcus, Maier Andreas, Rose Georg
2023-Jan-13
Breast imaging, Compression, Deep-learning, Mammography
