The Segmentation Model for Breast Cancer Ultrasound Image based on Attention U-Net

Jun Cheng

doi:10.54254/2755-2721/2025.CH23275

Research Article

Open access

Published on 19 May 2025

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Cheng,J. (2025). The Segmentation Model for Breast Cancer Ultrasound Image based on Attention U-Net. Applied and Computational Engineering,156,1-10.

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The Segmentation Model for Breast Cancer Ultrasound Image based on Attention U-Net

Jun Cheng *^,1,

¹ Mongolia University of Technology Hohhot, Hohhot City, Inner Mongolia Autonomous Region, 010080, China

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/2025.CH23275

Abstract

The segmentation of ultrasound image for breast cancer is an important task in the field of biomedical research. The traditional U-Net model, with its simple structure and remarkable performance, this approach has found extensive application in the segmentation of medical images. However, U-Net tends to be affected by background noise when handling images with complex backgrounds or blurry boundaries, which may impact the segmentation accuracy. To address this issue, the Attention U-Net model incorporates an attention mechanism, enabling the model to selectively focus on critical target areas within the image, thereby improving segmentation accuracy. This paper further optimizes the Attention U-Net architecture by increasing the depth of both the encoder and decoder sections, enhancing the model's capacity for feature extraction and image reconstruction. Consequently, both the accuracy and robustness of segmentation are enhanced. The experimental findings indicate that the proposed modified Attention U-Net model significantly outperforms traditional methods in breast ultrasound image segmentation tasks. It effectively handles various types of breast images, particularly those with complex backgrounds, blurred targets, or small sizes, maintaining high segmentation accuracy. This study offers an effective solution for the automated segmentation of breast ultrasound images, with substantial implications for enhancing both the automation and diagnostic efficiency in medical image analysis.

Keywords

Attention U-Net, image segmentation, ultrasound image, breast cancer

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References

[1]. R. Guo, G. Lu, B. Qin, and B. Fei, “Ultrasound imaging technologies for breast cancer detection and management: A review,” Ultrasound in Medicine & Biology, vol. 44, no. 1, pp. 37–70, Jan. 2018, doi: 10.1016/j.ultrasmedbio.2017.09.012.

[2]. O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, N. Navab, J. Hornegger, W. M. Wells, and A. F. Frangi, Eds., Cham: Springer International Publishing, 2015, pp. 234–241. doi: 10.1007/978-3-319-24574-4_28.

[3]. O. Oktay et al., “Attention U-net: Learning where to look for the pancreas,” May 20, 2018, arXiv: arXiv:1804.03999. doi: 10.48550/arXiv.1804.03999.

[4]. M. Zeenath and M. A. Ali, “A review paper on image segmentation and its various techniques in image processing,” International Journal of Research, 2014, Accessed: Feb. 07, 2025. [Online]. Available: https://www.semanticscholar.org/paper/A-Review-Paper-on-Image-Segmentation-and-its-in-Zeenath-Ali/67ac7045ee6f52823929d5caeddb390d06451f0e

[5]. H. G. Kaganami and Z. Beiji, “Region-based segmentation versus edge detection,” in 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Sep. 2009, pp. 1217–1221. doi: 10.1109/IIH-MSP.2009.13.

[6]. A. S. Kornilov and I. V. Safonov, “An overview of watershed algorithm implementations in open source libraries,” Journal of Imaging, vol. 4, no. 10, Art. no. 10, Oct. 2018, doi: 10.3390/jimaging4100123.

[7]. J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 3431–3440. Accessed: Feb. 07, 2025. [Online]. Available: https://openaccess.thecvf.com/content_cvpr_2015/html/Long_Fully_Convolutional_Networks_2015_CVPR_paper.html

[8]. S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137–1149, Jun. 2017, doi: 10.1109/TPAMI.2016.2577031.

[9]. W. Al-Dhabyani, M. Gomaa, H. Khaled, and A. Fahmy, “Dataset of breast ultrasound images,” Data in Brief, vol. 28, p. 104863, Feb. 2020, doi: 10.1016/j.dib.2019.104863.

[10]. R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-CAM: Visual explanations from deep networks via gradient-based localization,” Int J Comput Vis, vol. 128, no. 2, pp. 336–359, Feb. 2020, doi: 10.1007/s11263-019-01228-7.

Cite this article

Cheng,J. (2025). The Segmentation Model for Breast Cancer Ultrasound Image based on Attention U-Net. Applied and Computational Engineering,156,1-10.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of CONF-SEML 2025 Symposium: Intefrating AI into Software Engineering

Conference website: https://www.confseml.org/chicago.html

ISBN：978-1-80590-129-7(Print) / 978-1-80590-130-3(Online)

Conference date: 14 February 2025

Editor：Jie Zhang, Marwan Omar

Series: Applied and Computational Engineering

Volume number: Vol.156

ISSN：2755-2721(Print) / 2755-273X(Online)

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