Breast Anatomy Enriched Tumor Saliency Estimation

TL;DR

This paper introduces a novel tumor saliency estimation model for breast ultrasound that leverages enriched breast anatomy knowledge and a new background map to improve tumor localization accuracy.

Contribution

The study proposes a breast anatomy guided tumor saliency model with a new background map, enhancing tumor detection performance in ultrasound images.

Findings

Outperforms four state-of-the-art models by 10% F-measure on BUS dataset.

Integrates deep neural network generated layers with non-semantic breast anatomy models.

Introduces a weighted background map based on semantic probability and spatial distance.

Abstract

Breast cancer investigation is of great significance, and developing tumor detection methodologies is a critical need. However, it is a challenging task for breast ultrasound due to the complicated breast structure and poor quality of the images. In this paper, we propose a novel tumor saliency estimation model guided by enriched breast anatomy knowledge to localize the tumor. Firstly, the breast anatomy layers are generated by a deep neural network. Then we refine the layers by integrating a non-semantic breast anatomy model to solve the problems of incomplete mammary layers. Meanwhile, a new background map generation method weighted by the semantic probability and spatial distance is proposed to improve the performance. The experiment demonstrates that the proposed method with the new background map outperforms four state-of-the-art TSE models with increasing 10% of F_meansure on the BUS public dataset.