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Donghyun Kim

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Proceedings Article | 29 May 2024 Paper

A YOLO-based learning lesion classifier of pre-exposure scan in digital breast tomosynthesis

Seoyoung Lee, Subong Hyun, Donghyun Kim, Seungryong Cho

Proceedings Volume 13174, 131740C (2024) https://doi.org/10.1117/12.3025819

KEYWORDS: Digital breast tomosynthesis, X-rays, Monte Carlo methods, Image processing, Breast, Mammography, Object detection, Cancer detection

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Digital breast tomosynthesis (DBT) is an emerging x-ray breast imaging modality that scans the breast from multiple angles, allowing reconstruction of the breast's interior into a pseudo-3D image. While optimization variables in mammography are limited to x-ray tube voltage and exposure, DBT offers additional optimization possibilities such as scan angular range. Previous studies have established that wide-angle DBT excels in detecting larger objects, such as tumors, while narrow-angle DBT is superior in detecting smaller structures such as microcalcifications. Therefore, it would be advantageous to choose an option between narrow- and wide-angle scans in a patient-specific manner. In this study, we propose a method that utilizes pre-exposure scan data obtained during the automatic exposure control (AEC) process immediately before actual DBT scanning to predict patient lesion information in advance. We generated standard dose mammography and DBT pre-exposure scan using Monte Carlo-based numerical simulation. We trained a U-Net with added WGAN loss using this pair. Using this model, we synthesized pseudo-pre-exposure images from a real mammography dataset. Subsequently, a YOLO-based classification network was employed to distinguish whether masses were present or absent in the corresponding pre-exposure images. The trained network demonstrated an accuracy of 0.87 and an AUROC of 0.95, which is comparable to those of a classifier network using conventional mammography. A paired t-test also suggests that there is no statistically significant difference between the classifiers (t = 0.22). This study may contribute to enhancing breast cancer detection performance by proposing a patient-specific DBT scan range option.

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