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Garcia, E.B.G.* ; Lladó, X.* ; Mann, R.M.* ; Osuala, R. ; Martí, R.*

Breast composition measurements from Full-Field Digital Mammograms using generative adversarial networks.

In: (17th International Workshop on Breast Imaging, IWBI 2024, 9-12 June 2024, Chicago, US). 1000 20th St, Po Box 10, Bellingham, Wa 98227-0010 Usa: SPIE, 2024.:131740S (Proc. SPIE ; 13174)
DOI
Breast density has demonstrated to be an important risk factor for the development of breast cancer and, therefore, different fully automated density assessment tools have been introduced to obtain quantitative glandular tissue measures. Density maps (DMs) provide local tissue information, representing the amount of glandular tissue between the image receptor and the x-ray source at every pixel in the image. Usually, DMs are obtained from for processing, i.e. raw, mammograms. This fact could become a tricky problem because this type of images are not preserved in the clinical setting. The aim of this work is to introduce a deep learning based framework to synthesize glandular tissue DMs from for presentation mammograms. First, the breast region is located using a dedicated object detector network. Next, a generative adversarial network is used to obtain synthetic density maps, that are useful to evaluate not only the glandular tissue distribution but also the total glandular tissue volume within the breast. Results show that synthetic DMs obtain a structural similarity index of SSIM = 0.93 ± 0.06 with respect to real images. Similarly, shared information between the real and synthetic images, computed using the histogram intersection, corresponds to HI = 0.84 ± 0.10, while the average pixel difference represents only 3.85 ± 2.78 % of breast thickness. Furthermore, glandular tissue volume (GTV) obtained from synthetic density map show a strong correlation with the value provided by the real one (ρ = 0.89 [C.I 0.87 − 0.91]). In conclusion, generative deep learning models can be useful to evaluate breast composition, from local to global tissue distribution.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Breast Density ; Deep Learning ; Density Maps ; Full-field Digital Mammography ; Image Synthesis; Cancer Risk; Density
ISSN (print) / ISBN 0277-786X
e-ISSN 1996-756X
Konferenztitel 17th International Workshop on Breast Imaging, IWBI 2024
Konferzenzdatum 9-12 June 2024
Konferenzort Chicago, US
Zeitschrift Proceedings of SPIE
Quellenangaben Band: 13174, Heft: , Seiten: , Artikelnummer: 131740S Supplement: ,
Verlag SPIE
Verlagsort 1000 20th St, Po Box 10, Bellingham, Wa 98227-0010 Usa
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)
Förderungen AI algorithms in Breast Cancer
Ministry of Science, Innovation, and Universities of Spain