Nipple detection in breast dynamic contrast-enhanced magnetic resonance imaging using reinforcement learning

Motivation: As the nipple position knowledge becomes part of standardized report, the automatic detection can ease clinician’s workflow. Goal(s): The aim of our work is to accurately detect the position of the nipples in a dynamic contrast-enhanced (DCE) MR image. Approach: A reinforcement learning approach combined with a multi-constructor and multi-centric database enabled to initiate the development of a versatile tool in line with clinical real life. The detection problem was addressed using a Deep Q-Network trained with 248 breast DCE MR images. Results: The nipple positioning error is less than 10 millimeters in most of the breasts tested, i.e. 95/102 breasts. Impact: Nipple detection is a tedious task for clinicians and an arduous one for algorithms. Lesion to nipple distance is valuable information when planning surgery. This study explores the landmark detection domain to automate nipple detection using a reinforcement learning approach.