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

Seoyoung Lee; Subong Hyun; Donghyun Kim; Seungryong Cho

doi:10.1117/12.3025819

29 May 2024 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, 17th International Workshop on Breast Imaging (IWBI 2024); 131740C (2024) https://doi.org/10.1117/12.3025819
Event: 17th International Workshop on Breast Imaging (IWBI 2024), 2024, Chicago, IL, United States

Abstract

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.

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

Citation Download Citation

Seoyoung Lee, Subong Hyun, Donghyun Kim, and Seungryong Cho "A YOLO-based learning lesion classifier of pre-exposure scan in digital breast tomosynthesis", Proc. SPIE 13174, 17th International Workshop on Breast Imaging (IWBI 2024), 131740C (29 May 2024); https://doi.org/10.1117/12.3025819

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Digital breast tomosynthesis

Breast

Image processing

Monte Carlo methods

X-rays

Mammography

Object detection

Show All Keywords

Keywords/Phrases

Search In:

Publication Years