Clinical prototype implementation enabling an improved day-to-day mammography compression.
Breast imaging
Compression
Deep-learning
Mammography
Journal
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)
ISSN: 1724-191X
Titre abrégé: Phys Med
Pays: Italy
ID NLM: 9302888
Informations de publication
Date de publication:
Feb 2023
Feb 2023
Historique:
received:
20
04
2022
revised:
22
12
2022
accepted:
02
01
2023
pubmed:
16
1
2023
medline:
11
2
2023
entrez:
15
1
2023
Statut:
ppublish
Résumé
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. 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. 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. This prototype could help guiding to an improved breast compression routine in mammography procedures.
Identifiants
pubmed: 36641900
pii: S1120-1797(23)00001-7
doi: 10.1016/j.ejmp.2023.102524
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
102524Informations de copyright
Copyright © 2023 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: The authors Madeleine Hertel, Marcus Radicke, Haobo Song, Steffen Kappler and Ralf Nanke are employees of the Siemens Healthcare GmbH.