Skip to main content
Springer Nature Link
Log in

Breast Cancer Segmentation Using UNet and Global Convolutional Networks

  • Conference paper
  • First Online:
Pattern Recognition (ICPR 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15313))

Included in the following conference series:

  • 189 Accesses

Abstract

Breast ultrasound (BUS) imaging techniques have become efficient tools for cancer diagnosis. Convolutional neural network (CNN) based encoder-decoder architectures have been widely used for the automated segmentation of tumours in BUS images, assisting in breast cancer diagnoses. However, these models have limitations in capturing long-range dependencies. To overcome this limitation, various deep learning techniques, such as atrous convolution, attention mechanisms, and transformer encoder-based models, have been introduced to capture long-range dependencies in feature maps, improving segmentation accuracy by considering larger receptive fields and global context. As modelling techniques evolve, there is a shift towards more complex and intricate designs. This study proposes a simple yet effective model that combines UNet and Global Convolutional Network (GCN) architectures for breast lesion segmentation. By leveraging the GCN block, our model captures broader receptive fields with a simpler design strategy. We have demonstrated the efficacy of our approach through various experiments, including kernel size analysis, model component evaluation, and data preprocessing assessment. The proposed model has been evaluated using four-fold cross-validation with BUSI and Dataset-B datasets. Additionally, models trained on both datasets have been validated with a blind test dataset, where our model demonstrates better performance compared to state-of-the-art methods, achieving a 4.9% and 6.7% improvement in Intersection over Union (IoU) score, respectively. The robustness analysis and external validation experiments underscore the superior generalization performance of our model in breast lesion segmentation tasks.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Jemal, A., Bray, F., Center, M.M., Ferlay, J., Ward, E., Forman, D.: Global cancer statistics. CA: Cancer J. Clin. 61(2), 69–90 (2011)

    Google Scholar 

  2. Chan, V., Perlas, A.: Basics of ultrasound imaging. Atlas of ultrasound-guided procedures in interventional pain management, pp. 13–19 (2011)

    Google Scholar 

  3. Zhou, Z., et al.: Semi-automatic breast ultrasound image segmentation based on mean shift and graph cuts. Ultrason. Imaging 36(4), 256–276 (2014)

    Article  Google Scholar 

  4. Pons, G., Martí, J., Martí, R., Ganau, S., Noble, J.A.: Breast-lesion segmentation combining b-mode and elastography ultrasound. Ultrason. Imaging 38(3), 209–224 (2016)

    Article  Google Scholar 

  5. Xian, M., Zhang, Y., Cheng, H.D., Xu, F., Zhang, B., Ding, J.: Automatic breast ultrasound image segmentation: a survey. Pattern Recogn. 79, 340–355 (2018)

    Article  Google Scholar 

  6. Chen, G., Li, L., Dai, Y., Zhang, J., Yap, M.H.: AAU-net: an adaptive attention u-net for breast lesions segmentation in ultrasound images. IEEE Trans. Med. Imaging (2022)

    Google Scholar 

  7. Yan, Y., Liu, Y., Wu, Y., Zhang, H., Zhang, Y., Meng, L.: Accurate segmentation of breast tumors using ae u-net with HDC model in ultrasound images. Biomed. Signal Process. Control 72, 103299 (2022)

    Article  Google Scholar 

  8. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  9. Huang, H., et al.: Unet 3+: a full-scale connected unet for medical image segmentation. In: 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2020, pp. 1055–1059. IEEE (2020)

    Google Scholar 

  10. Huang, R., et al.: Boundary-rendering network for breast lesion segmentation in ultrasound images. Med. Image Anal. 80, 102478 (2022)

    Article  Google Scholar 

  11. Oktay, O., B., et al.: Attention u-net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)

  12. Zhang, X., et al.: Attention to region: region-based integration-and-recalibration networks for nuclear cataract classification using as-oct images. Med. Image Anal. 80, 102499 (2022)

    Article  Google Scholar 

  13. Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39(6), 1856–1867 (2019)

    Article  Google Scholar 

  14. Almajalid, R., Shan, J., Du, Y., Zhang, M.: Development of a deep-learning-based method for breast ultrasound image segmentation. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1103–1108. IEEE (2018)

    Google Scholar 

  15. Byra, M., et al.: Breast mass segmentation in ultrasound with selective kernel u-net convolutional neural network. Biomed. Signal Process. Control 61, 102027 (2020)

    Article  Google Scholar 

  16. Chen, G., Li, L., Zhang, J., Dai, Y.: Rethinking the unpretentious u-net for medical ultrasound image segmentation. Pattern Recogn. 142, 109728 (2023)

    Article  Google Scholar 

  17. Shareef, B., Xian, M., Vakanski, A.: Stan: small tumor-aware network for breast ultrasound image segmentation. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1–5. IEEE (2020)

    Google Scholar 

  18. Chen, G., Liu, Y., Dai, Y., Zhang, J., Cui, L., Yin, X.: Bagnet: bidirectional aware guidance network for malignant breast lesions segmentation. In: 2022 7th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS), pp. 112–116. IEEE (2022)

    Google Scholar 

  19. Lee, H., Park, J., Hwang, J.Y.: Channel attention module with multiscale grid average pooling for breast cancer segmentation in an ultrasound image. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 67(7), 1344–1353 (2020)

    Google Scholar 

  20. Abraham, N., Khan, N.M.: A novel focal Tversky loss function with improved attention u-net for lesion segmentation. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 683–687. IEEE (2019)

    Google Scholar 

  21. Qin, X., Zhang, Z., Huang, C., Gao, C., Dehghan, M., Jagersand, M.: Basnet: boundary-aware salient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7479–7489 (2019)

    Google Scholar 

  22. Wang, Y., et al.: Deeply-supervised networks with threshold loss for cancer detection in automated breast ultrasound. IEEE Trans. Med. Imaging 39(4), 866–876 (2019)

    Article  Google Scholar 

  23. Punn, N.S., Agarwal, S.: RCA-IUnet: a residual cross-spatial attention-guided inception u-net model for tumor segmentation in breast ultrasound imaging. Mach. Vis. Appl. 33(2), 27 (2022)

    Article  Google Scholar 

  24. Zhuang, Z., Li, N., Joseph Raj, A.N., Mahesh, V.G., Qiu, S.: An RDAU-net model for lesion segmentation in breast ultrasound images. PLoS ONE 14(8), e0221535 (2019)

    Article  Google Scholar 

  25. Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)

    Article  Google Scholar 

  26. Cao, X., Chen, H., Li, Y., Peng, Y., Wang, S., Cheng, L.: Dilated densely connected u-net with uncertainty focus loss for 3D ABUS mass segmentation. Comput. Methods Programs Biomed. 209, 106313 (2021)

    Article  Google Scholar 

  27. Chen, G., Dai, Y., Zhang, J.: C-net: cascaded convolutional neural network with global guidance and refinement residuals for breast ultrasound images segmentation. Comput. Methods Programs Biomed. 225, 107086 (2022)

    Article  Google Scholar 

  28. Chen, G., Yin, J., Dai, Y., Zhang, J., Yin, X., Cui, L.: A novel convolutional neural network for kidney ultrasound images segmentation. Comput. Methods Programs Biomed. 218, 106712 (2022)

    Article  Google Scholar 

  29. Irfan, R., Almazroi, A.A., Rauf, H.T., Damaševičius, R., Nasr, E.A., Abdelgawad, A.E.: Dilated semantic segmentation for breast ultrasonic lesion detection using parallel feature fusion. Diagnostics 11(7), 1212 (2021)

    Article  Google Scholar 

  30. Hu, Y., et al.: Automatic tumor segmentation in breast ultrasound images using a dilated fully convolutional network combined with an active contour model. Med. Phys. 46(1), 215–228 (2019)

    Article  Google Scholar 

  31. Li, C., Wang, X., Liu, W., Latecki, L.J., Wang, B., Huang, J.: Weakly supervised mitosis detection in breast histopathology images using concentric loss. Med. Image Anal. 53, 165–178 (2019)

    Article  Google Scholar 

  32. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)

    Google Scholar 

  33. Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 3–19 (2018)

    Google Scholar 

  34. Xue, C., et al.: Global guidance network for breast lesion segmentation in ultrasound images. Med. Image Anal. 70, 101989 (2021)

    Article  Google Scholar 

  35. Peng, C., Zhang, X., Yu, G., Luo, G., Sun, J.: Large kernel matters–improve semantic segmentation by global convolutional network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4353–4361 (2017)

    Google Scholar 

  36. Li, M.Y., Zhu, D.J., Xu, W., Lin, Y.J., Yung, K.L., Ip, A.W.: Application of u-net with global convolution network module in computer-aided tongue diagnosis. J. Healthcare Eng. 2021(1), 5853128 (2021)

    Google Scholar 

  37. Ulyanov, D., Vedaldi, A., Lempitsky, V.: Improved texture networks: maximizing quality and diversity in feed-forward stylization and texture synthesis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6924–6932 (2017)

    Google Scholar 

  38. Hooley, R.J., Scoutt, L.M., Philpotts, L.E.: Breast ultrasonography: state of the art. Radiology 268(3), 642–659 (2013)

    Article  Google Scholar 

  39. Gonzalez, R.C.: Digital Image Processing. Pearson Education India (2009)

    Google Scholar 

  40. Zhao, Z., Yang, L., Long, S., Pi, J., Zhou, L., Wang, J.: Augmentation matters: a simple-yet-effective approach to semi-supervised semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11350–11359 (2023)

    Google Scholar 

  41. Hofmanninger, J., Prayer, F., Pan, J., Röhrich, S., Prosch, H., Langs, G.: Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem. Eur. Radiol. Exp. 4, 1–13 (2020)

    Article  Google Scholar 

  42. De Boer, P.T., Kroese, D.P., Mannor, S., Rubinstein, R.Y.: A tutorial on the cross-entropy method. Ann. Oper. Res. 134, 19–67 (2005)

    Article  MathSciNet  Google Scholar 

  43. Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32 (2019)

    Google Scholar 

  44. Al-Dhabyani, W., Gomaa, M., Khaled, H., Fahmy, A.: Dataset of breast ultrasound images. Data Brief 28, 104863 (2020)

    Article  Google Scholar 

  45. Yap, M.H., et al.: Breast ultrasound region of interest detection and lesion localisation. Artif. Intell. Med. 107, 101880 (2020)

    Article  Google Scholar 

  46. Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to express their sincere gratitude to Prof. Hema A. Murthy, Emeritus Professor, Department of Computer Science and Engineering, IIT Madras, for her invaluable guidance and support throughout the completion of this research. The authors also extend their heartfelt thanks to IITM Pravartak Technologies Foundation, a Technology Innovation Hub of the Indian Institute of Technology, Madras, funded by the Department of Science and Technology, Government of India, under its National Mission on Interdisciplinary Cyber-Physical Systems, for supporting Anand Thyagachandran through a fellowship grant.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai, India

    Anand Thyagachandran & Yeruru Asrar Ahmed

Authors
  1. Anand Thyagachandran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yeruru Asrar Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anand Thyagachandran .

Editor information

Editors and Affiliations

  1. University of Salford, Salford, Lancashire, UK

    Apostolos Antonacopoulos

  2. IIT Bombay, Powai, Mumbai, Maharashtra, India

    Subhasis Chaudhuri

  3. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

  4. Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  5. IIT Kharagpur, Kharagpur, West Bengal, India

    Saumik Bhattacharya

  6. Indian Statistical Institute, Kolkata, West Bengal, India

    Umapada Pal

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Thyagachandran, A., Ahmed, Y.A. (2025). Breast Cancer Segmentation Using UNet and Global Convolutional Networks. In: Antonacopoulos, A., Chaudhuri, S., Chellappa, R., Liu, CL., Bhattacharya, S., Pal, U. (eds) Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15313. Springer, Cham. https://doi.org/10.1007/978-3-031-78201-5_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-78201-5_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-78200-8

  • Online ISBN: 978-3-031-78201-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships