Breast density follow-up decision support system using deep convolutional models

Sun Young Park; Dustin Sargent; David Richmond

doi:10.1117/12.2513047

13 March 2019 Breast density follow-up decision support system using deep convolutional models

Sun Young Park, Dustin Sargent, David Richmond

Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 109500G (2019) https://doi.org/10.1117/12.2513047
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Breast cancer risk assessment relies on accurate classification of breast density, which is a key component of the ACR breast cancer screening recommendations for clinical decisions. The 5^th edition of the BIRADS standard divides breast density into four categories, ranging from almost entirely fatty to extremely dense. High breast density (classes C and D) reduces the sensitivity of mammography, since the dense fibroglandular tissue can hide lesions, masses and other findings. Therefore, although the benefit of supplementary imaging in such cases has not been conclusively demonstrated, the ACR guidelines suggest additional screening for patients with high breast density. This creates an important treatment decision boundary between class B (scattered areas of fibroglandular density) and class C (heterogeneously dense). Unfortunately, the slightly abstract, qualitative nature of the class descriptions leads to significant inter- and intra-rater variation in breast density assessment. This is exacerbated by updates to the BIRADS standard that can cause recent breast density assessments to be incompatible with prior assessments for the same patient. Additionally, images from similar patients can vary significantly when taken with different devices or at sites with different acquisition protocols. To address these issues, we present a new deep learning algorithm combining three models that achieves accurate and objective breast density classification. The first model performs the normal four-class breast density classification, the second model performs a two-class low (A or B) vs. high (C or D) classification, and the third patch-based model focuses on improving the accuracy of the B and C categories. We present initial results from 9989 studies from a three-site dataset with BIRADS 4^th and 5^th edition ground truth.

Conference Presentation

Citation Download Citation

Sun Young Park, Dustin Sargent, and David Richmond "Breast density follow-up decision support system using deep convolutional models", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109500G (13 March 2019); https://doi.org/10.1117/12.2513047

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