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

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.