A deep learning method for volumetric breast density estimation from processed full field digital mammograms

Doiriel Vanegas C.; Mahlet A. Birhanu; Nico Karssemeijer; Albert Gubern-Mérida; Michiel Kallenberg

doi:10.1117/12.2512818

13 March 2019 A deep learning method for volumetric breast density estimation from processed full field digital mammograms

Doiriel Vanegas C., Mahlet A. Birhanu, Nico Karssemeijer, Albert Gubern-Mérida, Michiel Kallenberg

Author Affiliations +

Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 109500F (2019) https://doi.org/10.1117/12.2512818
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Breast density is an important factor in breast cancer screening. Methods exist to measure the volume of dense breast tissue from 2D mammograms. However, these methods can only be applied to raw mammograms. Breast density classification methods that have been developed for processed mammograms are commonly based on radiologist Breast Imaging and Reporting Data System (BI-RADS) annotations. Unfortunately, such labels are subjective and may introduce personal bias and inter-reader discrepancy. In order to avoid such limitations, this paper presents a method for estimation of percent dense tissue volume (PDV) from processed full field digital mammograms (FFDM) using a deep learning approach. A convolutional neural network (CNN) was implemented to carry out a regression task of estimating PDV using density measurement on raw FFDM as a ground truth. The dataset used for training, validation, and testing (Set A) includes over 2000 clinical cases from 3 different vendors. Our results show a high correlation of the predicted PDV to raw measurements, with a Spearman’s correlation coefficient of r=0.925. The CNN was also tested on an independent set of 97 clinical cases (Set B) for which PDV measurements from FFDM and MRI were available. CNN predictions on Set B showed a high correlation with both raw FFDM and MRI data (r=0.897 and r=0.903, respectively). Set B had radiologist annotated BI-RADS labels, which agreed with the estimated values to a high degree, showing the ability of our CNN to make a distinction between different BI-RADS categories comparable to methods applied to raw mammograms.

Conference Presentation

Citation Download Citation

Doiriel Vanegas C., Mahlet A. Birhanu, Nico Karssemeijer, Albert Gubern-Mérida, and Michiel Kallenberg "A deep learning method for volumetric breast density estimation from processed full field digital mammograms", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109500F (13 March 2019); https://doi.org/10.1117/12.2512818

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