End-to-end mammographic breast density quantification with deep learning: preliminary study on simulated mammograms

Sjoerd A. M. Tunissen; Andrea Motta; Franziska Mauter; Eloy García; Oliver Diaz; John M. Boone; Ioannis Sechopoulos; Marco Caballo

doi:10.1117/12.2654131

7 April 2023 End-to-end mammographic breast density quantification with deep learning: preliminary study on simulated mammograms

Sjoerd A. M. Tunissen, Andrea Motta, Franziska Mauter, Eloy García, Oliver Diaz, John M. Boone, Ioannis Sechopoulos, Marco Caballo

Author Affiliations +

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 124650K (2023) https://doi.org/10.1117/12.2654131
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

High breast density (BD) is recognized as an independent risk factor for breast cancer development, in addition to negatively impacting the sensitivity of mammography. Although BD is normally assessed with the BI-RADS reporting system, this evaluation is qualitative and has been shown to vary considerably across readers. In this pilot study, we present a deep learning (DL) method to quantify BD from a standard two-view (cranio-caudal, and medio-lateral-oblique) mammography exam. With the aim of developing a method based on an objective ground truth, the DL model was trained and validated using 88 simulated mammograms from an equal number of distinct 3D digital breast phantoms for which BD is known. The phantoms had been previously generated through segmentation and simulated mechanical compression of patient dedicated breast CT images, allowing for the exact calculation of BD in each case. Different data augmentations were applied prior to simulation, to increase the dataset size, yielding a total of 528 cases. These were divided, randomly and on a patient level, into training (N=360), validation (N=60), and test sets (N=108). The DL model performance was tested by stratifying the breasts into four different density ranges: 1-15%, 15-25%, 25-60%, and <60%. The median absolute errors and interquartile ranges (IQR), in percentage points, were 3.3 (IQR: 3.5), 3.4 (IQR: 2.5), 3.5 (IQR: 3.9), and 14.8 (IQR: 8.4), respectively. Although preliminary, these results show the potential of the proposed approach for accurate BD quantification, which is based, as opposed to most previously proposed approaches, on an objective ground truth.

Conference Presentation

Citation Download Citation

Sjoerd A. M. Tunissen, Andrea Motta, Franziska Mauter, Eloy García, Oliver Diaz, John M. Boone, Ioannis Sechopoulos, and Marco Caballo "End-to-end mammographic breast density quantification with deep learning: preliminary study on simulated mammograms", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 124650K (7 April 2023); https://doi.org/10.1117/12.2654131

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