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

# metadata.dc.description.abstract[].value
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.

# metadata.dc.relation.isformatof[].value
Versió postprint de la comunicació publicada a: https://doi.org/10.1117/12.3026925

# metadata.dc.relation.ispartof[].value
Comunicació a: Proc. SPIE 13174, 17th International Workshop on Breast Imaging (IWBI 2024), 131740S (29 May 2024)

# metadata.dc.source[].value
Comunicacions a congressos  (Matemàtiques i Informàtica)

# metadata.dc.title[].value
Breast composition measurements from Full-Field Digital Mammograms using generative adversarial networks