Clinical prototype implementation enabling an improved day-to-day mammography compression

Hertel M, Liu C, Song H, Golatta M, Kappler S, Nanke R, Radicke M, Maier A, Rose G (2023)

Publication Type: Journal article

Publication year: 2023

Journal

Physica Medica Elsevier

Book Volume: 106

Article Number: 102524

DOI: 10.1016/j.ejmp.2023.102524

Abstract

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.

Authors with CRIS profile

Chang Liu Lehrstuhl für Informatik 5 (Mustererkennung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Universitätsklinikum Heidelberg DE Germany (DE) Siemens AG DE Germany (DE) Otto-von-Guericke-Universität Magdeburg DE Germany (DE)

How to cite

APA:

Hertel, M., Liu, C., Song, H., Golatta, M., Kappler, S., Nanke, R.,... Rose, G. (2023). Clinical prototype implementation enabling an improved day-to-day mammography compression. Physica Medica, 106. https://dx.doi.org/10.1016/j.ejmp.2023.102524

MLA:

Hertel, Madeleine, et al. "Clinical prototype implementation enabling an improved day-to-day mammography compression." Physica Medica 106 (2023).

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