SpringerLink
Log in

A novel method for glioma segmentation and classification on pre-operative MRI scans using 3D U-Nets and transfer learning

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This research paper proposes a novel approach that harnesses deep learning techniques to address two critical objectives in brain tumor analysis: segmentation and classification. We have used a 3D U-Net architecture to acquire spatial relationships and accurately delineate tumor regions from MRI images. These masks are superimposed onto the original images generating a lucid visualisation of the tumorous areas. For classification of tumors into HGG (high-grade gliomas) and LGG (low-grade gliomas) we created a customized CNN model and utilised transfer learning with VGG16, ResNet50, InceptionV3, MobileNetV2 and DenseNet121 as the pre-trained models. This approach has been tested using the BraTS 2019 dataset which bears a testimony to its cutting-edge performance in both segmentation and classification tasks. The denoising procedure yields an impressive average Peak Signal-to-Noise Ratio (PSNR) value of 97.82 dB, ensuring the production of denoised images of exceptional quality. The 3D U-Net employed for mask segmentation demonstrates precise delineation with a mean Intersection over Union (IoU) value of 0.97. Our custom CNN model exhibits exceptional efficacy, attaining training, validation and testing accuracies to be 97.99%, 97.99%, and 97.14% respectively. The proposed model has been compared with some of the state-of-the-art techniques and it has been found to outperform them as well. These findings underscore the model’s resilience, dependability, and potential to enhance brain tumor analysis, thereby facilitating accurate diagnosis and treatment planning.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Data availability

All the data and the codes are available with the authors and will be provided on reasonable request.

References

  1. Bukhari ST, Mohy-ud-Din H (2022) E1D3 U-Net for brain tumor segmentation: Submission to the RSNA-ASNR-MICCAI BraTS 2021 challenge. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part II (pp 276–288). Cham: Springer International Publishing

  2. Jiang Z, Ding C, Liu M, Tao D (2020) Two-stage cascaded u-net: 1st place solution to brats challenge 2019 segmentation task. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Revised Selected Papers, Part I 5 (pp 231–241). Springer International Publishing

  3. Henry T, Carré A, Lerousseau M, Estienne T, Robert C, Paragios N, Deutsch E (2021) Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part I 6 (pp. 327–339). Springer International Publishing

  4. Maurya S, Yadav K, Agarwal V, S., Singh A (2022) Brain tumor segmentation in mpmri scans (brats-2021) using models based on u-net architecture. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part II (pp 312–323). Cham: Springer International Publishing

  5. Jena B, Nayak GK, Paul S, Saxena S (2022) An exhaustive analytical study of U-Net architecture on two diverse biomedical imaging datasets of electron microscopy drosophila ssTEM and brain MRI BraTS-2021 for segmentation. SN Comput Sci 3(5):418

    Article  Google Scholar 

  6. Jena B, Jain S, Nayak GK, Saxena S (2023) Analysis of depth variation of U-NET architecture for brain tumor segmentation. Multimed Tools Appl 82(7):10723–10743

    Article  Google Scholar 

  7. Shaukat Z, Farooq QUA, Tu S, **ao C, Ali S (2022) A state-of-the-art technique to perform cloud-based semantic segmentation using deep learning 3D U-Net architecture. BMC Bioinformatics 23(1):251

    Article  Google Scholar 

  8. Mehta R, Arbel T (2019) 3D U-Net for brain tumour segmentation. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part II 4 (pp 254–266). Springer International Publishing

  9. Das S, Swain MK, Nayak GK, Saxena S, Satpathy SC (2022) Effect of learning parameters on the performance of U-Net model in segmentation of brain tumor. Multimed Tools Appl 81(24):34717–34735

    Article  Google Scholar 

  10. Rawat A, Kumar R (2022) Assessing layer normalization with brats mri data in a convolution neural net. In: Computational Intelligence in Data Science: 5th IFIP TC 12 International Conference, ICCIDS 2022, Virtual Event, March 24–26, 2022, Revised Selected Papers (pp 124–135). Springer International Publishing, Cham

  11. Tene-Hurtado D, Almeida-Galárraga DA, Villalba-Meneses G, Alvarado-Cando O, Cadena-Morejón C, Salazar VH, ..., Tirado-Espín A (2022) Brain tumor segmentation based on 2D U-Net using MRI multi-modalities brain images. In: Smart Technologies, Systems and Applications: Second International Conference, SmartTech-IC 2021, Quito, Ecuador, December 1–3, 2021, Revised Selected Papers. Springer International Publishing, Cham, pp 345–359 

  12. Chen M, Wu Y, Wu J (2020) Aggregating multi-scale prediction based on 3D U-Net in brain tumor segmentation. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Revised Selected Papers, Part I 5. Springer International Publishing, pp 142–152

  13. Dong H, Yang G, Liu F, Mo Y, Guo Y (2017) Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks. In: Medical Image Understanding and Analysis: 21st Annual Conference, MIUA 2017, Edinburgh, UK, July 11–13, 2017, Proceedings 21. Springer International Publishing, pp 506–517

  14. Kermi A, Mahmoudi I, Khadir MT (2019) Deep convolutional neural networks using U-Net for automatic brain tumor segmentation in multimodal MRI volumes. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part II 4. Springer International Publishing, pp 37–48

  15. Tang J, Li T, Shu H, Zhu H (2021) Variational-autoencoder regularized 3D MultiResUNet for the BraTS 2020 brain tumor segmentation. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part II 6. Springer International Publishing, pp 431–440

  16. Druzhinina P, Kondrateva E, Bozhenko A, Yarkin V, Sharaev M, Kurmukov A (2022) BRATS2021: Exploring each sequence in multi-modal input for baseline u-net performance. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part I. Springer International Publishing, Cham, pp 194–203

  17. Punn NS, Agarwal S (2021) Multi-modality encoded fusion with 3D inception U-Net and decoder model for brain tumor segmentation. Multimed Tools Appl 80(20):30305–30320

    Article  Google Scholar 

  18. Li H, Nan Y, Yang G (2022) LKAU-Net: 3D large-kernel attention-based u-net for automatic MRI brain tumor segmentation. In: Medical Image Understanding and Analysis: 26th Annual Conference, MIUA 2022, Cambridge, UK, July 27–29, 2022, Proceedings. Springer International Publishing, Cham, pp 313–327

  19. Le HT, Pham HTT (2020) Brain tumor segmentation using U-Net based deep neural networks. In: 7th International Conference on the Development of Biomedical Engineering in Vietnam (BME7) Translational Health Science and Technology for Develo** Countries, vol 7. Springer Singapore, pp 39–42

  20. Akter A, Nosheen N, Ahmed S, Hossain M, Yousuf MA, Almoyad MAA, ..., Moni MA (2024) Robust clinical applicable CNN and U-Net based algorithm for MRI classification and segmentation for brain tumor. Expert Syst Appl 238:122347

  21. Faysal Ahamed M, Robiul Islam M, Hossain T, Syfullah K, Sarkar O (2023) Classification and segmentation on multi-regional brain tumors using volumetric images of MRI with customized 3D U-Net framework. In: Proceedings of International Conference on Information and Communication Technology for Development: ICICTD 2022. Springer Nature Singapore, Singapore, pp 223–234

  22. Chen W, Liu B, Peng S, Sun J, Qiao X (2019) S3D-UNet: separable 3D U-Net for brain tumor segmentation. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part II 4. Springer International Publishing, pp 358–368

  23. Ali S, Li J, Pei Y, Rehman KU (2023) A multi-module 3d u-net learning architecture for brain tumor segmentation. In: Data Mining and Big Data: 7th International Conference, DMBD 2022, Bei**g, China, November 21–24, 2022, Proceedings, Part II. Springer Nature Singapore, Singapore, pp 57–69

  24. Khorasani A, Kafieh R, Saboori M, Tavakoli MB (2022) Glioma segmentation with DWI weighted images, conventional anatomical images, and post-contrast enhancement magnetic resonance imaging images by U-Net. Phys Eng Sci Med 45(3):925–934

    Article  Google Scholar 

  25. Ghosh S, Chaki A, Santosh KC (2021) Improved U-Net architecture with VGG-16 for brain tumor segmentation. Phys Eng Sci Med 44(3):703–712

    Article  Google Scholar 

  26. Trivedi H, Thumar K, Ghelani K, Gandhi D (2021) An analysis of brain tumor segmentation using modified u-net architecture. In: Innovations in Information and Communication Technologies (IICT-2020) Proceedings of International Conference on ICRIHE-2020, Delhi, India: IICT-2020. Springer International Publishing, pp 141–147

  27. Rosas González S, Sekou B, Hidane T, M., Tauber C (2020) 3D automatic brain tumor segmentation using a multiscale input U-Net network. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Revised Selected Papers, Part II 5. Springer International Publishing, pp 113–123

  28. Sun J, Peng Y, Li D, Guo Y (2021) Segmentation of the multimodal brain tumor images used Res-U-Net. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part I 6. Springer International Publishing, pp 263–273

  29. Bhalerao M, Thakur S (2020) Brain tumor segmentation based on 3D residual U-Net. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Revised Selected Papers, Part II. Springer International Publishing, Cham, pp 218–225

  30. Cai X, Lou S, Shuai M, An Z (2022) Feature learning by attention and ensemble with 3d u-net to glioma tumor segmentation. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part II. Springer International Publishing, Cham, pp 68–79

  31. Rani R, Dev A, Sharma A, Joshi P (2022) Implementation and empirical analysis of deep learning models for COVID-19 detection and lung disease prediction. In: 2022 IEEE Delhi Section Conference (DELCON), pp 1–6. IEEE

  32. Singhania A, Narera A, Rani R, Dev A, Bansal P, Sharma A (2022) Computer-aided breast cancer diagnosis using deep convolutional neural networks. In: 2022 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), pp 787–791. IEEE

  33. Kaur M, Mann P, Rani R, Jaiswal G, Sharma A, Kumar R (2023) MRI Image segmentation using deep learning for brain tumor detection. 2023 10th International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India, pp 939–944

  34. Rahman AU, Saeed M, Saeed MH, Zebari DA, Albahar M, Abdulkareem KH, ..., Mohammed MA (2023) A framework for susceptibility analysis of brain tumours based on uncertain analytical cum algorithmic modeling. Bioengineering 10(2):147

  35. Zebari NA, Mohammed CN, Zebari DA, Mohammed MA, Zeebaree DQ, Marhoon HA, ..., Martinek R (2024) A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images. CAAI Transactions on Intelligence Technology

  36. Ra**ikanth V, Kadry S, Damaševičius R, Sujitha RA, Balaji G, Mohammed MA (2022) Glioma/glioblastoma detection in brain MRI using pre-trained deep-learning scheme. In: 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT), pp 987–990. IEEE

  37. Husham S, Mustapha A, Mostafa SA, Al-Obaidi MK, Mohammed MA, Abdulmaged AI, George ST (2020) Comparative analysis between active contour and otsu thresholding segmentation algorithms in segmenting brain tumor magnetic resonance imaging. J Inform Technol Manag 12:48–61. (Special Issue: Deep Learning for Visual Information Analytics and Management)

  38. Mijwil MM, Doshi R, Hiran KK, Unogwu OJ, Bala I (2023) MobileNetV1-based deep learning model for accurate brain tumor classification. Mesopotamian J Comput Sci 2023:32–41

    Article  Google Scholar 

  39. Al-Shahwani HIW, Faieq AK (2023) The benefit of artificial intelligence in the analysis of malignant brain diseases: a mini review. Mesopotamian J Artif Intell Healthc 2023:57–60

    Google Scholar 

  40. Ali AM, Mohammed MA (2024) A comprehensive review of artificial intelligence approaches in omics data processing: evaluating progress and challenges. Int J Math Stat Comput Sci 2:114–167

    Article  Google Scholar 

  41. Cho HH, Park H (2017) Classification of low-grade and high-grade glioma using multi-modal image radiomics features. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp 3081–3084). IEEE

  42. Ge C, Qu Q, Gu IYH, Jakola AS (2018) 3D multi-scale convolutional networks for glioma grading using MR images. In: 2018 25th IEEE international conference on image processing (ICIP), pp 141–145. IEEE

  43. Ge C, Gu IYH, Jakola AS, Yang J (2018) Deep learning and multi-sensor fusion for glioma classification using multistream 2D convolutional networks. In: 2018 40th Annual international conference of the IEEE engineering in medicine and biology society (EMBC) (pp 5894–5897). IEEE

  44. Bi X, Liu JG, Cao YS (2019) Classification of low-grade and high-grade glioma using multiparametric radiomics model. In: 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pp 574–577. IEEE

Download references

Funding

None.

Author information

Authors and Affiliations

  1. Indira Gandhi Delhi Technical University for Women, Delhi, 110008, India

    Gaurisha R. Srivastava, Pooja Gera, Ritu Rani & Arun Sharma

  2. Bennett University, Greater Noida, India

    Garima Jaiswal

Authors
  1. Gaurisha R. Srivastava
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pooja Gera
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ritu Rani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Garima Jaiswal
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Arun Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ritu Rani.

Ethics declarations

Consent for publication

Not applicable.

Ethical approval

Not applicable.

Competing interests

The authors declare that they have no conflicts of interest related to this study.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Srivastava, G.R., Gera, P., Rani, R. et al. A novel method for glioma segmentation and classification on pre-operative MRI scans using 3D U-Nets and transfer learning. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19261-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11042-024-19261-1

Keywords

Search

Navigation