Mssa-Net: Multi-Scale Self-Attention Network For Breast Ultrasound Image Segmentation
Meng Xu, Kuan Huang, Qiuxiao Chen, Xiaojun Qi
Abstract
Ultrasound imaging is one of the most commonly used diagnostic tools to detect and classify abnormalities of the women breast. Automatic ultrasound image segmentation provides radiologists a second opinion to increase diagnosis accuracy. Deep neural networks have recently been employed to achieve better image segmentation results than conventional approaches. In this paper, we propose a novel deep learning architecture, a Multi-Scale Self-Attention Network (MSSA-Net), which can be trained on small datasets to explore relationships between pixels to achieve better segmentation accuracy. Our MSSA-Net integrates rich local features and global contextual information at different scales and applies self-attention to multi-scale feature maps. We evaluate the proposed MSSA-Net on three public breast ultrasound datasets and compare its performance with six state-of-the-art deep neural network-based approaches in terms of five metrics. MSSA-Net achieves best overall segmentation results and improves the second best approach by 1.21% for Jaccard Index (JI) and 0.94% for Dice's Coefficient (DSC).