Fully automated breast density assessment from low-dose chest CT

Shuang Liu; Laurie R. Margolies; Yiting Xie; David F. Yankelevitz; Claudia I. Henschke; Anthony P. Reeves

doi:10.1117/12.2254151

3 March 2017 Fully automated breast density assessment from low-dose chest CT

Shuang Liu, Laurie R. Margolies, Yiting Xie, David F. Yankelevitz, Claudia I. Henschke, Anthony P. Reeves

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 101340R (2017) https://doi.org/10.1117/12.2254151
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Breast cancer is the most common cancer diagnosed among US women and the second leading cause of cancer death 1 . Breast density is an independent risk factor for breast cancer and more than 25 states mandate its reporting to patients as part of the lay mammogram report ² . Recent publications have demonstrated that breast density measured from low-dose chest CT (LDCT) correlates well with that measured from mammograms and MRIs ^3-4 , thereby providing valuable information for many women who have undergone LDCT but not recent mammograms. A fully automated framework for breast density assessment from LDCT is presented in this paper. The whole breast region is first segmented using an anatomy-orientated novel approach based on the propagation of muscle fronts for separating the fibroglandular tissue from the underlying muscles. The fibroglandular tissue regions are then identified from the segmented whole breast and the percentage density is calculated based on the volume ratio of the fibroglandular tissue to the local whole breast region. The breast region segmentation framework was validated with 1270 LDCT scans, with 96.1% satisfactory outcomes based on visual inspection. The density assessment was evaluated by comparing with BI-RADS density grades established by an experienced radiologist in 100 randomly selected LDCT scans of female subjects. The continuous breast density measurement was shown to be consistent with the reference subjective grading, with the Spearman’s rank correlation 0.91 (p-value < 0.001). After converting the continuous density to categorical grades, the automated density assessment was congruous with the radiologist’s reading in 91% cases.

Conference Presentation

Citation Download Citation

Shuang Liu, Laurie R. Margolies, Yiting Xie, David F. Yankelevitz, Claudia I. Henschke, and Anthony P. Reeves "Fully automated breast density assessment from low-dose chest CT", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101340R (3 March 2017); https://doi.org/10.1117/12.2254151

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