Segmentation Algorithm of Breast Tumor in Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based on Network with Multi-scale Residuals and Dual-domain Attention

LIU Xia; LÜ Zhiwei; LI Bo; WANG Bo; WANG Di

doi:10.11999/JEIT220362

Volume 45 Issue 5

May 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(5): 1774-1785

LIU Xia, LÜ Zhiwei, LI Bo, WANG Bo, WANG Di. Segmentation Algorithm of Breast Tumor in Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based on Network with Multi-scale Residuals and Dual-domain Attention[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1774-1785. doi: 10.11999/JEIT220362

Citation:

LIU Xia, LÜ Zhiwei, LI Bo, WANG Bo, WANG Di. Segmentation Algorithm of Breast Tumor in Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based on Network with Multi-scale Residuals and Dual-domain Attention[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1774-1785. doi: 10.11999/JEIT220362

Citation:

LIU Xia, LÜ Zhiwei, LI Bo, WANG Bo, WANG Di. Segmentation Algorithm of Breast Tumor in Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based on Network with Multi-scale Residuals and Dual-domain Attention[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1774-1785. doi: 10.11999/JEIT220362

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Segmentation Algorithm of Breast Tumor in Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based on Network with Multi-scale Residuals and Dual-domain Attention

doi: 10.11999/JEIT220362

LIU Xia^{1, 2},
LÜ Zhiwei¹,
LI Bo¹,
WANG Bo^{3
,
,},
WANG Di¹

1.
School of Automation, Harbin University of Science and Technology, Harbin 150080, China
2.
Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, Harbin 150080, China
3.
Computer Engineering Technical College (Artificial Intelligence College), Guangdong Polytechnic of Science and Technology, Zhuhai 519090, China

Funds: The National Natural Science Foundation of China (61172167), The Youth Science Foundation of Heilongjiang Province (QC2017076)

Received Date: 2022-03-31
Rev Recd Date: 2022-07-15

Available Online: 2022-07-26

Publish Date: 2023-05-10

Abstract

Abstract

Considering the problems of breast tumor size and shape change, blurred boundary and severe class imbalance between foreground and background, a multi-scale residual dual-domain attention fusion network is proposed. In this network, multi-scale residual blocks composed of multi-scale convolution are used as the basic building modules. Multi-scale residual block improves the network's ability to recognize targets of different sizes and the model’s robustness by extracting multi-scale features and optimizing gradient propagation. Meanwhile, the dual-domain attention units are integrated into the network to improve the ability of edge recognition and boundary preservation. The hybrid loss function with adaptive weight is proposed, it can improve the optimization direction of the network, alleviate the influence of the extreme imbalance of positive and negative samples. The experimental results show that the average Dice value of the method proposed in this paper reaches 0.8063, which is 5.3% higher than that of U-shaped Network (UNet), and the number of parameters is reduced by 73.36%, which has better segmentation performance.
- Breast tumor segmentation,
- Multi-scale residual block,
- Dual-domain attention,
- Hybrid loss function with adaptive weight

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References(26)

References

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