Convolutional-neural-network based breast thickness correction in digital breast tomosynthesis

Seoyoung Lee; Hyeongseok Kim; Hoyeon Lee; Uijin Jeong; Seungryong Cho

doi:10.1117/12.2560909

22 May 2020 Convolutional-neural-network based breast thickness correction in digital breast tomosynthesis

Seoyoung Lee, Hyeongseok Kim, Hoyeon Lee, Uijin Jeong, Seungryong Cho

Proceedings Volume 11513, 15th International Workshop on Breast Imaging (IWBI2020); 115131E (2020) https://doi.org/10.1117/12.2560909
Event: Fifteenth International Workshop on Breast Imaging, 2020, Leuven, Belgium

Abstract

This work addresses equalization and thickness estimation of breast periphery in digital breast tomosynthesis (DBT). Breast compression in DBT would lead to a relatively uniform thickness at inner breast but not at the periphery. Proper peripheral enhancement or thickness correction is needed for diagnostic convenience and for accurate volumetric breast density estimation. Such correction methods have been developed albeit with several shortcomings. We present a thickness correction method based on a supervised learning scheme with a convolutional neural network (CNN), which is one of the widely-used deep learning structures, to improve the pixel value of the peripheral region. The network was successfully trained and showed a robust and satisfactory performance in our numerical phantom study.

Citation Download Citation

Seoyoung Lee, Hyeongseok Kim, Hoyeon Lee, Uijin Jeong, and Seungryong Cho "Convolutional-neural-network based breast thickness correction in digital breast tomosynthesis", Proc. SPIE 11513, 15th International Workshop on Breast Imaging (IWBI2020), 115131E (22 May 2020); https://doi.org/10.1117/12.2560909

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