AI-based automated breast cancer segmentation in ultrasound imaging based on Attention Gated Multi ResU-Net

被引:0
作者
Ding, Ting [1 ,2 ]
Shi, Kaimai [3 ]
Pan, Zhaoyan [4 ]
Ding, Cheng [5 ]
机构
[1] School of Earth Science, East China University of Technology, JiangXi,Nanhang
[2] Urumqi Comprehensive Survey Center on Natural Resources, XinJiang, Urumq
[3] School of Physics, Georgia Institution of Technology, Atlanta, GA
[4] School of Energy Power Engineering, Xian Jiaotong University, Xian
[5] Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA
基金
中国国家自然科学基金;
关键词
Breast cancer; Deep learning; Medical image segmentation; Ultrasound imaging;
D O I
10.7717/PEERJ-CS.2226
中图分类号
学科分类号
摘要
Breast cancer is a leading cause of death among women worldwide, making early detection and diagnosis critical for effective treatment and improved patient outcomes. Ultrasound imaging is a common diagnostic tool for breast cancer, but interpreting ultrasound images can be challenging due to the complexity of breast tissue and the variability of image quality. This study proposed an Attention Gated Multi ResU-Net model for medical image segmentation tasks, that has shown promising results for breast cancer ultrasound image segmentation. The model’s multi-scale feature extraction and attention-gating mechanism enable it to accurately identify and segment areas of abnormality in the breast tissue, such as masses, cysts, and calcifications. The model’s quantitative test showed an adequate degree of agreement with expert manual annotations, demonstrating its potential for improving early identification and diagnosis of breast cancer. The model’s multi-scale feature extraction and attention-gating mechanism enable it to accurately identify and segment areas of abnormality in the breast tissue, such as masses, cysts, and calcifications, achieving a Dice coefficient of 0.93, sensitivity of 93%, and specificity of 99%. These results underscore the model’s high precision and reliability in medical image analysis. © 2024 Ding et al.
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