Automated deep-learning method for whole-breast segmentation in diffusion-weighted breast MRI

Abstract

The essential sequences in breast magnetic resonance imaging (MRI) are the dynamic contrast-enhanced (DCE) images, which are widely used in clinical settings. Diffusion-weighted imaging (DWI) MRI also plays an important role in many diagnostic applications and in developing novel imaging bio-makers. Compared to DCE MRI, technical advantages of DWI include a shorter acquisition time, no need for administration of any contrast agent, and availability on most commercial scanners. Segmenting the whole-breast region is an essential pre-processing step in many quantitative and radiomics breast MRI studies. However, it is a challenging task for computerized methods due to the low contrast of intensity along breast chest wall boundaries. While several studies have reported computational methods for automated whole-breast segmentation in DCE MRI, the segmentation in DWI MRI is still underdeveloped. In this paper, we propose to use deep learning and transfer learning methods to segment the whole-breast in DWI MRI, by leveraging pretraining on a DCE MRI dataset. Experiments are reported in multiple breast MRI datasets including an external evaluation dataset and encouraging results are demonstrated.