Automatic assessment of mammographic images : positioning and quality assessment

Abstract

Breast cancer is a global challenge, causing over 1 million deaths in 2018 and affecting millions more. Screening mammograms to detect breast cancer in its early stages is an extremely vital step for prevention and treatment. However, to maximize the efficacy of mammography-based screening for breast cancer, proper positioning and quality is of utmost importance. Improper positioning could result in missed cancers or might require return patient visits for additional imaging. Therefore, assessment of quality at the first visit prior to examination of the mammogram by a radiologist is a crucial step in accurate cancer detection. This study proposes multiple deep learning techniques combined with geometric evaluations to provide numerical metrics on the quality of mammographic images. The study found that using a RetinaNet model to detect breast landmarks achieved high precision in the mediolateral oblique view (92% for muscle top and 51% for muscle bottom) and 83% in detecting the nipple in both the mediolateral oblique and craniocaudal view. Using these detected landmarks, we provide a report containing numerical metrics on positioning evaluations of the breast images for mammography technologists to use during the patient visit to avoid fallbacks of improper positioning. This report could aid technologists in taking proper precautions to help radiologists effectively detect breast cancer.