For women of high risk ($>25%$ lifetime risk) for developing Breast Cancer combination screening of mammography and magnetic resonance imaging (MRI) is recommended. Risk stratification is based on current modeling tools for risk assessment. However, adding additional radiological features may improve AUC. To validate tissue features in MRI requires large scale epidemiological studies across health centres. Therefore it is essential to have a robust, fully automated segmentation method. This presents a challenge of imaging domain adaptation in deep learning. Here, we present a breast segmentation pipeline that uses multiple UNet segmentation models trained on different image types. We use Monte-Carlo Dropout to measure each model's uncertainty allowing the most appropriate model to be selected when the image domain is unknown. We show our pipeline achieves a dice similarity average of 0.78 for fibroglandular tissue segmentation and has good adherence to radiologist assessment.