Breast mass segmentation based on ultrasonic entropy maps and attention gated U-Net

TL;DR

This paper introduces a deep learning approach using entropy maps and attention U-Net for breast mass segmentation in ultrasound images, showing improved performance over traditional US image-based methods.

Contribution

It presents a novel segmentation method leveraging quantitative entropy parametric maps with attention U-Net, outperforming US image-based models.

Findings

Entropy maps improved segmentation accuracy.

Attention U-Net achieved higher Dice scores with entropy maps.

Quantitative US parametric maps are promising for breast mass segmentation.

Abstract

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.