A Systematic Review of Convolutional Neural Network for Brain Tumor Segmentation in MRI

Danesh, Hajar; Ghasemi , Zahra

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Volume 33, Issue 5 (11-2025)

Journal of Ilam University of Medical Sciences 2025, 33(5): 53-80

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A Systematic Review of Convolutional Neural Network for Brain Tumor Segmentation in MRI

Hajar Danesh ^*¹

, Zahra Ghasemi²

1- Dept of Electrical and Biomedical Engineering, Faculty of Engineering and ‎Technology, Shahid Ashrafi Esfahani University, Isfahan, Iran , hajardanesh@yahoo.com
2- Dept of Electrical and Biomedical Engineering, Faculty of Engineering and ‎Technology, Shahid Ashrafi Esfahani University, Isfahan, Iran

Abstract: (164 Views)

Introduction: Brain tumors are caused by abnormal cell proliferation and can lead to neurological disorders by affecting the structure and function of the brain. Therefore, their accurate and timely diagnosis plays a significant role in reducing clinical risks for patients. Magnetic resonance imaging, as a non-invasive and highly accurate method, is widely used in identifying tumor areas in the diagnosis and treatment planning process.
Materials & Methods: This study is a qualitative systematic review based on the PRISMA guideline, which was conducted by comprehensively searching the Web of Science, Google Scholar, Springer, Scopus, IEEE Xplore, and Elsevier scientific databases. Studies related to convolutional neural networks (CNN) for brain tumor segmentation in Magnetic Resonance Imaging (MRI) images, focusing on advanced architectures including U-Net, nnU-Net, V-Net, DeepMedic, and DeepLabV3+, were selected based on specific inclusion and exclusion criteria and subjected to qualitative and comparative analysis.
Results: The results of the reviewed articles showed that the DeepLabV3+ model had the highest accuracy with an average Dice score of 0.917 and the other models U-Net, nnU-Net, V-Net, and DeepMedic had average Dice scores of 0.827, 0.793, 0.819, and 0.752, respectively. All of these models performed better than manual or traditional methods in different data conditions such as 2D, 3D, and unbalanced data. However, the reported performance for each model is affected by several factors, including the quality and volume of training data, data augmentation strategies, the loss function used, and post-processing steps.
Conclusion: This study shows that novel image analysis methods significantly improve the accuracy of diagnostic assistance systems in brain imaging by automatically extracting diagnostic features. However, the performance of the models is the result of a combination of the main architecture and complementary techniques, and their evaluation should be performed within the overall framework of the analysis pipeline (from preprocessing to post-processing). Developing models with high generalizability in diverse data conditions is the main path of progress in this field. Given the time-consuming and complex nature of manual interpretation of MRI images, deep learning-based systems can help reduce human errors and facilitate clinical decision-making. Consequently, optimization and development of these models is an important step towards improving the diagnosis and management of patients with brain tumors.

Keywords: Brain tumor segmentation, Convolutional neural network, Magnetic resonance imaging, Deep learning

Full-Text [PDF 1852 kb] (114 Downloads)

Type of Study: Research | Subject: Surgical nursing
Received: 2025/07/13 | Accepted: 2025/09/30 | Published: 2025/11/26

References

1. Khazaei Z, Goodarzi E, Borhaninejad V, Iranmanesh F, Mirshekarpour H, Mirzaei B, et al. The association between incidence and mortality of brain cancer and human development index (HDI): an ecological study. BMC Public Health. 2020;20:1696. doi:10.1186/s12889-020-09838-4.

2. Wild CP, Stewart BW, editors. World cancer report 2014. Geneva, Switzerland: World Health Organization; 2014.

3. Ranjbarzadeh R, Caputo A, Tirkolaee EB, Ghoushchi SJ, Bendechache M. Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools. Comput Biol Med. 2023; 152:106405. doi: 10.1016/j.compbiomed.2022.106405.

4. Jiao C, Yang T. An overview of multimodal brain tumor MR image segmentation methods. In: Third International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 2023; 12717. Bellingham, WA: SPIE; 2023. p. 725–33. doi:10.1117/12.2685325.

5. Anantharajan S, Gunasekaran S, Subramanian T. MRI brain tumor detection using deep learning and machine learning approaches. Meas Sens. 2024; 31:101026. doi: 10.1016/j.measen.2024.101026.

6. Ilhan U, Ilhan A. Brain tumor segmentation based on a new threshold approach. Procedia Comput Sci. 2017; 120:580-7. doi: 10.1016/j.procs.2017.11.282.

7. Jardim S, António J, Mora C. Image thresholding approaches for medical image segmentation—short literature review. Procedia Comput Sci. 2023; 219:1485-92. doi: 10.1016/j.procs.2023.01.439

8. Kashyap R, Gautam P. Modified region-based segmentation of medical images. In: 2015 International Conference on Communication Networks (ICCN); 2015 Nov 19; Gwalior, India. Piscataway, NJ: IEEE; 2015. p. 209-16. doi:10.1109/ICCN.2015.41.

9. Liu HX, Fang JX, Zhang ZJ, Lin YC. Localised edge‐region‐based active contour for medical image segmentation. IET Image Process. 2021;15:1567-82. doi:10.1049/ipr2.12126.

10. Abidin ZU, Naqvi RA, Haider A, Kim HS, Jeong D, Lee SW. Recent deep learning-based brain tumor segmentation models using multi-modality magnetic resonance imaging: A prospective survey. Front Bioeng Biotechnol. 2024; 12:1392807. doi:10.3389/fbioe.2024.1392807.

11. Swathi VN, Sinduja K, Kumar VR, Mahendar A, Prasad GV, Samya B. Deep learning-based brain tumor detection: An MRI segmentation approach. MATEC Web Conf. 2024; 392:01157. doi:10.1051/matecconf/202439201157.

12. Shrestha A, Mahmood A. Review of deep learning algorithms and architectures. IEEE Access. 2019; 7:53040-65. doi:10.1109/ACCESS.2019.2912200.

13. Shinde PP, Shah S. A review of machine learning and deep learning applications. In: 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA); 2018 Aug 16; Pune, India. Piscataway, NJ: IEEE; 2018. p. 1–6. doi:10.1109/ICCUBEA.2018.8697857.

14. Ganaie MA, Hu M, Malik AK, Tanveer M, Suganthan PN. Ensemble deep learning: A review. Eng Appl Artif Intell. 2022; 115:105151. doi: 10.1016/j.engappai.2022.105151.

15. Samee NA, Ahmad T, Mahmoud NF, Atteia G, Abdallah HA, Rizwan A. Clinical decision support framework for segmentation and classification of brain tumor MRIs using a U-Net and DCNN cascaded learning algorithm. Healthcare. 2022;10:2340. doi:10.3390/healthcare10122340.

16. Kamnitsas K, Ferrante E, Parisot S, Ledig C, Nori AV, Criminisi A, et al. DeepMedic for brain tumor segmentation. In: International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries; 2016 Oct 16; Athens, Greece. Cham: Springer International Publishing; 2017. p. 138–49. doi:10.1007/978-3-319-55524-9_14.

17. Rehman MU, Cho S, Kim JH, Chong KT. Bu-net: Brain tumor segmentation using modified u-net architecture. Electronics. 2020;9:2203. doi:10.3390/electronics9122203.

18. Chen S, Ding C, Liu M. Dual-force convolutional neural networks for accurate brain tumor segmentation. Pattern Recognit. 2019; 88:90-100. doi: 10.1016/j.patcog.2018.11.009.

19. Kermi A, Mahmoudi I, Khadir MT. Deep convolutional neural networks using U-Net for automatic brain tumor segmentation in multimodal MRI volumes. In: International MICCAI Brainlesion Workshop; 2018 Sep 16; Granada, Spain. Cham: Springer International Publishing; 2019. p. 37-48. doi:10.1007/978-3-030-11726-9_4.

20. Türkmen Y. Brain lesion segmentation using deep learning [Master’s thesis]. Ankara: Ankara Üniversitesi; 2023.

21. Raza R, Bajwa UI, Mehmood Y, Anwar MW, Jamal MH. dResU-Net: 3D deep residual U-Net based brain tumor segmentation from multimodal MRI. Biomed Signal Process Control. 2023; 79:103861. doi: 10.1016/j.bspc.2022.103861.

22. Aboussaleh I, Riffi J, El Fazazy K, Mahraz AM, Tairi H. 3DUV-NetR+: A 3D hybrid semantic architecture using transformers for brain tumor segmentation with MultiModal MR images. Results Eng. 2024; 21:101892. doi: 10.1016/j.rineng.2024.101892.

23. Kharaji M, Abbasi H, Orouskhani Y, Shomalzadeh M, Kazemi F, Orouskhani M. Brain tumor segmentation with advanced nnU-Net: pediatrics and adults tumors. Neurosci Inform. 2024;4:100156. doi: 10.1016/j.neuri.2024.100156.

24. Bonato B. From BraTS Challenges to an Extended Glioma Dataset: State-of-the-Art BrainSegFounder Model Optimization and a Decade of Insights into Multi-Class Glioma Tumor Segmentation.

25. Hashmi S, Lugo J, Elsayed A, Saggurthi D, Elseiagy M, Nurkamal A, et al. Optimizing brain tumor segmentation with MedNeXt: BraTS 2024 SSA and pediatrics. arXiv [preprint]. 2024. arXiv:2411.15872. doi:10.48550/arXiv.2411.15872.

26. Vossough A, Khalili N, Familiar AM, Gandhi D, Viswanathan K, Tu W, et al. Training and comparison of nnU-Net and DeepMedic methods for autosegmentation of pediatric brain tumors. AJNR Am J Neuroradiol. 2024;45:1081-9. doi:10.3174/ajnr.A8293.

27. Kazerooni AF, Khalili N, Liu X, Haldar D, Jiang Z, Zapaishchykova A, et al. BraTS-PEDs: Results of the multi-consortium international pediatric brain tumor segmentation challenge 2023. arXiv [preprint]. 2024. arXiv:2407.08855. doi:10.48550/arXiv.2407.08855.

28. Asiri AA, Shaf A, Ali T, Aamir M, Irfan M, Alqahtani S, et al. Brain tumor detection and classification using fine-tuned CNN with ResNet50 and U-Net model: A study on TCGA-LGG and TCIA dataset for MRI applications. Life. 2023;13:1449. doi:10.3390/life13071449.

29. Aish MA, Iqbal A, Ahmad J, Nasim F. Brain MRI Classification and Segmentation on TCGA-LGG and TCIA Dataset. JICET. 2025; 5: 1-16.

30. Shomirov A, Zhang J, Billah MM. Brain tumor segmentation of HGG and LGG MRI images using WFL-based 3D U-net. J Biomed Sci Eng. 2022;15:241-60.

31. Mutasa S, Sun S, Ha R. Understanding artificial intelligence-based radiology studies: CNN architecture. Clin Imaging. 2021; 80:72–6. doi: 10.1016/j.clinimag.2021.06.033.

32. Anwar SM, Majid M, Qayyum A, Awais M, Alnowami M, Khan MK. Medical image analysis using convolutional neural networks: a review. J Med Syst. 2018;42:226. doi:10.1007/s10916-018-1088-1.

33. Kumar A. Study and analysis of different segmentation methods for brain tumor MRI application. Multimed Tools Appl. 2023;82:7117-39. doi:10.1007/s11042-022-13636-y.

34. Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI); 2015; Munich, Germany. Cham: Springer International Publishing; 2015. p. 234-41. doi:10.1007/978-3-319-24574-4_28.

35. Isensee F, Jäger PF, Full PM, Vollmuth P, Maier-Hein KH. nnU-Net for brain tumor segmentation. In: International MICCAI Brainlesion Workshop; 2020 Oct 4; Lima, Peru. Cham: Springer International Publishing; 2021. p. 118-32. doi:10.1007/978-3-030-72087-2_11.

36. Huang L, Miron A, Hone K, Li Y. Segmenting medical images: from UNet to res-UNet and nnUNet. In: 2024 IEEE 37th International Symposium on Computer-Based Medical Systems. IEEE. 2024; 483-9. doi:10.1109/CBMS61543.2024.00086.

37. Jyothi CK, Awati A, Torse D. Optimizing Brain Tumor Segmentation in MRI images with Enhanced nnU-Net. In: 2024 Second International Conference on Networks, Multimedia and Information Technology (NMITCON); 2024 Aug 9; Belgaum, India. Piscataway, NJ: IEEE; 2024. p. 1-6. doi:10.1109/NMITCON62075.2024.10698771.

38. Guan X, Yang G, Ye J, Yang W, Xu X, Jiang W, et al. 3D AGSE-VNet: an automatic brain tumor MRI data segmentation framework. BMC Med Imaging. 2022;22:6. doi:10.1186/s12880-021-00728-8.

39. Chen W, Liu B, Peng S, Sun J, Qiao X. S3D-UNet: separable 3D U-Net for brain tumor segmentation. In: International MICCAI Brainlesion Workshop; 2018 Sep 16; Granada, Spain. Cham: Springer International Publishing; 2019. p. 358-68. doi:10.1007/978-3-030-11726-9_32.

40. Hua R, Huo Q, Gao Y, Sun Y, Shi F. Multimodal brain tumor segmentation using cascaded V-Nets. In: International MICCAI Brainlesion Workshop; 2018 Sep 16; Granada, Spain. Cham: Springer International Publishing; 2019. p. 49-60. doi:10.1007/978-3-030-11726-9_5.

41. Hua R, Huo Q, Gao Y, Sui H, Zhang B, Sun Y, et al. Segmenting brain tumor using cascaded V-Nets in multimodal MR images. Front Comput Neurosci. 2020; 14:9. doi:10.3389/fncom.2020.00009.

42. Chandra S, Vakalopoulou M, Fidon L, Battistella E, Estienne T, Sun R, et al. Context aware 3D CNNs for brain tumor segmentation. In: International MICCAI Brainlesion Workshop; 2018 Sep 16; Granada, Spain. Cham: Springer International Publishing; 2019. p. 299–310. doi:10.1007/978-3-030-11726-9_27.

43. Rahmat R, Saednia K, Khani MR, Rahmati M, Jena R, Price SJ. Multi-scale segmentation in GBM treatment using diffusion tensor imaging. Comput Biol Med. 2020; 123:103815. doi: 10.1016/j.compbiomed.2020.103815.

44. Kamnitsas K, Ledig C, Newcombe VF, Simpson JP, Kane AD, Menon DK, et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal. 2017; 36:61–78. doi: 10.1016/j.media.2016.10.004.

45. Saeed T, Khan MA, Hamza A, Shabaz M, Khan WZ, Alhayan F, et al. Neuro-XAI: Explainable deep learning framework based on DeeplabV3+ and Bayesian optimization for segmentation and classification of brain tumor in MRI scans. J Neurosci Methods. 2024; 410:110247. doi: 10.1016/j.jneumeth.2024.110247.

46. Berghout T. The neural frontier of future medical imaging: a review of deep learning for brain tumor detection. J Imaging. 2024;11:2. doi:10.3390/jimaging11010002.

47. Ahuja S, Panigrahi BK, Gandhi TK. Fully automatic brain tumor segmentation using DeepLabv3+ with variable loss functions. In: 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN); 2021 Aug 26; Noida, India. Piscataway, NJ: IEEE; 2021. p. 522–6. doi:10.1109/SPIN52536.2021.9566128.

48. Saifullah S, Dreżewski R, Yudhana A. Advanced brain tumor segmentation using DeepLabV3Plus with Xception encoder on a multi-class MR image dataset. Multimed Tools Appl. 2025:1-22. doi:10.1007/s11042-025-20702-8.

49. Piboonthummasak T, Yamcharoen N, Nobnop N, Charoenpong T. A Method for Brain Tumor Segmentation Using DeeplabV3+: Learning Rate Optimization. In: 2024 16th Biomedical Engineering International Conference (BMEiCON); 2024 Nov 21; Bangkok, Thailand. Piscataway, NJ: IEEE; 2024. p. 1–4. doi:10.1109/BMEiCON64021.2024.10896264.

50. Akagic A, Kapo M, Kandić E, Bećirović M, Kadrić N. Brain Tumor Segmentation of MRI Images with U-Net and DeepLabV3+. In: 2024 IEEE 3rd International Conference on Computing and Machine Intelligence (ICMI); 2024 Apr 13; Dhaka, Bangladesh. Piscataway, NJ: IEEE; 2024. p. 1–6. doi:10.1109/ICMI60790.2024.10585749.

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Danesh H, Ghasemi Z. A Systematic Review of Convolutional Neural Network for Brain Tumor Segmentation in MRI. J. Ilam Uni. Med. Sci. 2025; 33 (5) :53-80
URL: http://sjimu.medilam.ac.ir/article-1-8675-en.html

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