Skip to main content

Adaptive CNN Method for Prostate MR Image Segmentation Using Ensemble Learning

  • Conference paper
  • First Online:
Artificial Intelligence XLI (SGAI 2024)

Abstract

In 2020, there were more than 1.4 million new cases of prostate cancer worldwide, and more than 375,000 deaths from the disease. The conventional diagnostic pathway hinges on the assessment of prostate-specific antigen (PSA) levels and the conduct of trans-rectal ultrasound (TRUS)-guided biopsies. However, the specificity of PSA as a biomarker is notably low, at approximately 36%, due to its elevation in benign prostatic conditions, underscoring the imperative for more precise diagnostic modalities. This research leverages a dataset comprising T2-weighted magnetic resonance (MR) images from 1,151 patients, totaling 61,119 images, to refine prostate cancer diagnostics. This paper introduces methodology that utilises knowledge-based artificial intelligence (AI) frameworks with image segmentation techniques to enhance the accuracy of prostate cancer detection. The approach in this paper focuses on the segmentation of MR images into distinct anatomical zones of the prostate - specifically, the transition zone (TZ) and peripheral zone (PZ). The variations of model produce a Dice Similarity Coefficient in the range of 0.373–0.544 in the 95th percentile. This segmentation is critical for the automation and augmentation of diagnostic precision in prostate cancer. This approach not only aims to improve the specificity and sensitivity of prostate cancer diagnostics but also to facilitate the exploitation of publicly accessible datasets for research advancements in this domain.

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

Access this chapter

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. WCRF International. Prostate cancer statistics: World Cancer Research Fund International (2022). https://www.wcrf.org/cancer-trends/prostate-cancer-statistics/

  2. Schröder, F.H., et al.: Screening and prostate-cancer mortality in a randomized European study. New Engl. J. Med. 360(13), 1320–1328 (2009)

    Article  Google Scholar 

  3. Barentsz, J.O., et al.: ESUR prostate MR guidelines 2012. Eur. Radiol. 22(4), 746–757 (2012)

    Article  Google Scholar 

  4. Ahmed, H.U., et al.: Is it time to consider a role for MRI before prostate biopsy? Nat. Rev. Clin. Oncol. 6(4), 197–206 (2009)

    Article  Google Scholar 

  5. Huang, S., Yang, J., Fong, S., Zhao, Q.: Artificial intelligence in cancer diagnosis and prognosis: opportunities and challenges. Cancer Lett. 471, 61–71 (2020)

    Article  Google Scholar 

  6. Murphy, G., Haider, M., Ghai, S., Sreeharsha, B.: The expanding role of MRI in prostate cancer. AJR Am. J. Roentgenol. 201(6), 1229–38 (2013)

    Article  Google Scholar 

  7. Luo, R., Zeng, Q., Chen, H.: Artificial intelligence algorithm-based MRI for differentiation diagnosis of prostate cancer. Comput. Math. Methods Med. (2022)

    Google Scholar 

  8. Lawrentschuk, N., et al.:‘Prostatic evasive anterior tumours’: the role of magnetic resonance imaging. BJU Int. 105(9), 1231–1236 (2010)

    Google Scholar 

  9. Ardila, D., et al.: End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat. Med. 25(6), 954–961 (2019)

    Article  Google Scholar 

  10. Jacobson, L.E., Hopgood, A.A., Bader-El-Den, M., Tamma, V., Prendergast, D., Osborn, P.: Hybrid system for prostate MR image segmentation using expert knowledge and machine learning. In: Bramer, M., Stahl, F. (eds.) SGAI 2023. LNCS, vol. 14381, pp. 493–498. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-47994-6_43

    Chapter  Google Scholar 

  11. Anwar, S.M., Majid, M., Qayyum, A., Awais, M., Alnowami, M., Khan, M.K.: Medical image analysis using convolutional neural networks: a review. J. Med. Syst. 42(11), 1–13 (2018)

    Article  Google Scholar 

  12. Chahal, E.S., Patel, A., Gupta, A., Purwar, A.: Unet based Xception model for prostate cancer segmentation from MRI images. Multimed. Tools Appl. 81(26), 37333–37349 (2022)

    Article  Google Scholar 

  13. Siddique, N., Paheding, S., Elkin, C.P., Devabhaktuni, V.: U-net and its variants for medical image segmentation: a review of theory and applications. IEEE Access 9, 82031–82057 (2021)

    Article  Google Scholar 

  14. Aldoj, N., Biavati, F., Michallek, F., Stober, S., Dewey, M.: Automatic prostate and prostate zones segmentation of magnetic resonance images using DenseNet-like U-net. Sci. Rep. 10(1), 1–17 (2020)

    Article  Google Scholar 

  15. Ma, J.J.,et al.: Diagnostic image quality assessment and classification in medical imaging: opportunities and challenges. In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 337–340. IEEE (2020)

    Google Scholar 

  16. Litjens, G., Debats, O., Barentsz, J., Karssemeijer, N., Huisman, H.: Computer-aided detection of prostate cancer in MRI. IEEE Trans. Med. Imaging 33(5), 1083–1092 (2014)

    Article  Google Scholar 

  17. Masoudi, S., et al.: Quick guide on radiology image pre-processing for deep learning applications in prostate cancer research. J. Med. Imaging 8(1), 010901 (2021)

    Article  Google Scholar 

  18. Buda, M., Maki, A., Mazurowski, M.A.: A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw. 106, 249–259 (2018)

    Article  Google Scholar 

  19. He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263–1284 (2009)

    Article  Google Scholar 

  20. Yap, B.W., Rani, K.A., Rahman, H.A.A., Fong, S., Khairudin, Z., Abdullah, N.N.: An application of oversampling, undersampling, bagging and boosting in handling imbalanced datasets. In: Herawan, T., Deris, M.M., Abawajy, J. (eds.) Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013). LNEE, vol. 285, pp. 13–22. Springer, Singapore (2014). https://doi.org/10.1007/978-981-4585-18-7_2

    Chapter  Google Scholar 

  21. Haixiang, G., Yijing, L., Shang, J., Mingyun, G., Yuanyue, H., Bing, G.: Learning from class-imbalanced data: review of methods and applications. Expert Syst. Appl. 73, 220–239 (2017)

    Article  Google Scholar 

  22. Wang, K.J., Makond, B., Chen, K.H., Wang, K.M.: A hybrid classifier combining SMOTE with PSO to estimate 5-year survivability of breast cancer patients. Appl. Soft Comput. 20, 15–24 (2014)

    Article  Google Scholar 

  23. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)

    Article  Google Scholar 

  24. Bader-El-Den, M., Teitei, E., Perry, T.: Biased random forest for dealing with the class imbalance problem. IEEE Trans. Neural Netw. Learn. Syst. 30(7), 2163–2172 (2018)

    Article  Google Scholar 

  25. Safdar, K., Akbar, S., Shoukat, A.: A majority voting based ensemble approach of deep learning classifiers for automated melanoma detection. In: 2021 International Conference on Innovative Computing (ICIC), pp. 1–6. IEEE (2021)

    Google Scholar 

  26. Ju, C., Bibaut, A., van der Laan, M.: The relative performance of ensemble methods with deep convolutional neural networks for image classification. J. Appl. Stat. 45(15), 2800–2818 (2018)

    Article  MathSciNet  Google Scholar 

  27. Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)

    Article  Google Scholar 

  28. The Cancer Imaging Archive (TCIA). (2022). Prostate-MRI-US-Biopsy [Data file]. Retrieved from https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=68550661

  29. Bardis, M., et al.: Segmentation of the prostate transition zone and peripheral zone on MR images with deep learning. Radiol. Imaging Cancer, 3(3) (2021)

    Google Scholar 

  30. Bonekamp, D., Jacobs, M.A., El-Khouli, R., Stoianovici, D., Macura, K.J.: Advancements in MR imaging of the prostate: from diagnosis to interventions. RadioGraphics 31, 677–703 (2011)

    Article  Google Scholar 

  31. Wang, Z., Wu, R., Xu, Y., Liu, Y., Chai, R., Ma, H.: A two-stage CNN method for MRI image segmentation of prostate with lesion. Biomed. Signal Process. Control 82, 104610 (2023)

    Article  Google Scholar 

  32. Hassanzadeh, T., Hamey, L.G., Ho-Shon, K.: Convolutional neural networks for prostate magnetic resonance image segmentation. IEEE Access 7, 36748–36760 (2019)

    Article  Google Scholar 

  33. Negi, A., Raj, A.N.J., Nersisson, R., Zhuang, Z., Murugappan, M.: RDA-UNET-WGAN: an accurate breast ultrasound lesion segmentation using wasserstein generative adversarial networks. Arab. J. Sci. Eng. 45, 6399–6410 (2020)

    Article  Google Scholar 

  34. 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 

  35. Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)

    Article  Google Scholar 

  36. Tian, Z., Liu, L., Fei, B.: Deep convolutional neural network for prostate MR segmentation. Int. J. Comput. Assist. Radiol. Surg. 13(11), 1687 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lars E. O. Jacobson .

Editor information

Editors and Affiliations

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

Jacobson, L.E.O. et al. (2025). Adaptive CNN Method for Prostate MR Image Segmentation Using Ensemble Learning. In: Bramer, M., Stahl, F. (eds) Artificial Intelligence XLI. SGAI 2024. Lecture Notes in Computer Science(), vol 15447. Springer, Cham. https://doi.org/10.1007/978-3-031-77918-3_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-77918-3_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-77917-6

  • Online ISBN: 978-3-031-77918-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics