Detecting Suboptimal Breast Positioning in Screening Mammograms Using Neural Networks

Abstract

Proper breast positioning in screening mammography minimizes the likelihood that tissue is obscured or missing from a mammogram and decreases the rate of technical repeats and misdiagnosis. Several factors contribute to improper positioning including technologist experience, patient condition, and patient body habitus. One way to help the technologist identify and improve improper positioning is through MQSA’s required periodic image quality performance evaluations known as the EQUIP review. While valuable for performance improvement, the number of cases evaluated during these reviews is relatively small compared to the overall number of studies a technologist performs and may not provide the nuanced feedback needed for process improvement. In this study, we have developed convolutional neural network models to detect several aspects of suboptimal positioning as defined by the ACR’s positioning standards towards providing technologists valuable feedback for measuring performance improvement.The dataset contained a total of 600 clinical screening mammograms with a variety of positioning-related anomalies. An experienced technologist labeled relevant anomalies and image features important to breast positioning assessment. Image review tasks were separated into one of two categories: classification tasks and segmentation tasks. Using a neural network approach, segmentation models were developed to identify the pectoralis muscle boundary, skin folds, the inframammary fold (IMF), and nipple location. Classification models were developed to determine if the nipple was in profile, if the breast was droopy, and if the IMF was open, not open, or not shown. The classification models had a final accuracy of 89.3% for classifying whether the IMF was shown or not, 81.3% for determining if the IMF was shown and open, shown and not open, or not shown, and 80.2% for identifying if the nipple was in profile. These algorithms will ultimately be implemented to evaluate all screening mammograms acquired at Duke Health to automatically identify breast positioning features for clinical feedback.