  We propose a novel deep learning based approach to breast mass segmentation
in ultrasound (US) imaging. In comparison to commonly applied segmentation
methods, which use US images, our approach is based on quantitative entropy
parametric maps. To segment the breast masses we utilized an attention gated
U-Net convolutional neural network. US images and entropy maps were generated
based on raw US signals collected from 269 breast masses. The segmentation
networks were developed separately using US image and entropy maps, and
evaluated on a test set of 81 breast masses. The attention U-Net trained based
on entropy maps achieved average Dice score of 0.60 (median 0.71), while for
the model trained using US images we obtained average Dice score of 0.53
(median 0.59). Our work presents the feasibility of using quantitative US
parametric maps for the breast mass segmentation. The obtained results suggest
that US parametric maps, which provide the information about local tissue
scattering properties, might be more suitable for the development of breast
mass segmentation methods than regular US images.
