2026-04-17T06:35:43Zhttps://recercat.cat/oai/request

oai:recercat.cat:2445/2199792025-12-05T09:56:33Zcom_2072_1057col_2072_478917col_2072_478920

Breast composition measurements from Full-Field Digital Mammograms using generative adversarial networks García Marcos, Eloy Badó Llardera, Xavier Mann, Ritse M. Osuala, Richard Martí Marly, Robert Mamografia Aprenentatge automàtic Diagnòstic per la imatge Mammography Machine learning Diagnostic imaging 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 glandu- lar 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 syn- thetic 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. 2025-03-25T10:52:32Z 2025-03-25T10:52:32Z 2024 info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/acceptedVersion https://hdl.handle.net/2445/219979 eng Versió postprint de la comunicació publicada a: https://doi.org/10.1117/12.3026925 Comunicació a: Proc. SPIE 13174, 17th International Workshop on Breast Imaging (IWBI 2024), 131740S (29 May 2024) Proceedings SPIE 13174 https://doi.org/10.1117/12.3026925 (c) SPIE, 2024 info:eu-repo/semantics/openAccess 8 p. application/pdf Comunicacions a congressos (Matemàtiques i Informàtica)