Domain adapted breast tissue segmentation in magnetic resonance imaging

Grey Kuling; Belinda Curpen M.D.; Anne L. Martel

doi:10.1117/12.2564131

Translator Disclaimer

22 May 2020 Domain adapted breast tissue segmentation in magnetic resonance imaging

Grey Kuling, Belinda Curpen M.D., Anne L. Martel

Author Affiliations +

Proceedings Volume 11513, 15th International Workshop on Breast Imaging (IWBI2020); 115131B (2020) https://doi.org/10.1117/12.2564131
Event: Fifteenth International Workshop on Breast Imaging, 2020, Leuven, Belgium

Abstract

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.

Citation Download Citation

Grey Kuling, Belinda Curpen M.D., and Anne L. Martel "Domain adapted breast tissue segmentation in magnetic resonance imaging", Proc. SPIE 11513, 15th International Workshop on Breast Imaging (IWBI2020), 115131B (22 May 2020); https://doi.org/10.1117/12.2564131

ACCESS THE FULL ARTICLE