Correlation between mammographic GAN artifacts and patient characteristics

Ridhi Arora; Tamerlan Mustafaev; Juhun Lee

doi:10.1117/12.3008729

2 April 2024 Correlation between mammographic GAN artifacts and patient characteristics

Ridhi Arora, Tamerlan Mustafaev, Juhun Lee

Proceedings Volume 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications; 129310H (2024) https://doi.org/10.1117/12.3008729
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

In our previous study, we developed a Conditional GAN (CGAN) model, pix2pix, to simulate one side of a mammogram by using the other side as a condition image. Despite generating plausible mammograms, various artifacts appeared in some generated mammograms. As our model uses each woman’s breast mammogram as condition, patient-specific breast tissue characteristics can affect resulting GAN simulated mammograms, as well as artifacts. This study therefore analyzed the potential relationship of GAN generated mammographic artifacts with patient variables closely related to breast tissue characteristics, which are age and breast density (mammographic percent density). We trained our CGAN using Craniocaudal (CC) views of 1366 normal/healthy women. Using trained CGAN, we synthesized mammograms of 333 women with dense breasts, where 97 had unilateral mammographically-occult breast cancer. We found four artifact types, checkerboard, breast boundary, nipple-areola, and black spots around calcifications (black spots) – with an overall incidence rate of 69%. We then evaluated if there was a systematic difference in age and breast density of GAN simulated mammograms with and without artifacts. The results show no meaningful difference in age and breast density between the simulated mammograms with and without checkerboard artifact (p⪆0.05). For breast boundary and nipple areola artifacts, we found that those artifacts appeared more on denser breasts (p⪅0.001). For black spot artifacts, it appeared more on older women (p⪅0.003) and on less dense breasts (p⪅0.001). In summary, this study found that there is possible correlation between patient variables and the chance of having certain artifacts on CGAN simulated mammograms.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ridhi Arora, Tamerlan Mustafaev, and Juhun Lee "Correlation between mammographic GAN artifacts and patient characteristics", Proc. SPIE 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications, 129310H (2 April 2024); https://doi.org/10.1117/12.3008729

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