Artificial Intelligence for Brain Tumor Segmentation: A Review of 3D MRI-Based Models and Clinical Applications
Keywords:
Artificial Intelligence, Brain Tumor Segmentation, 3D MRI, Deep Learning, Clinical ApplicationsAbstract
Artificial intelligence (AI) has significantly advanced the field of brain tumor segmentation by enabling accurate and automated analysis of volumetric magnetic resonance imaging (MRI) data. Traditional manual segmentation is labor-intensive and subject to inter-observer variability, necessitating robust computational approaches. Recent developments in deep learning, particularly 3D convolutional neural networks, U-Net variants, and transformer-based architectures, have demonstrated superior capability in capturing complex spatial and contextual features of brain tumors. Benchmark initiatives such as the Brain Tumor Segmentation Challenge (BraTS) have facilitated standardized evaluation and accelerated innovation in this domain. These models have shown promising results in clinical applications, including tumor detection, treatment planning, and disease monitoring. However, challenges such as data heterogeneity, computational demands, and limited interpretability remain barriers to widespread clinical adoption. This review critically examines recent advancements in 3D MRI-based AI models and their translational potential in neuro-oncology.
References
Myronenko, A. (2018). 3D MRI brain tumor segmentation using autoencoder regularization. arXiv Preprint.
Hamghalam, M., Lei, B., & Wang, T. (2019). Brain tumor synthetic segmentation in 3D multimodal MRI scans. arXiv Preprint.
Fernando, K. R. M., & Tsokos, C. P. (2021). Deep and statistical learning in biomedical imaging: State of the art in 3D MRI brain tumor segmentation. arXiv Preprint.
Ahsan, M. M., Alam, T., Trafalis, T., & Huebner, P. (2020). Deep learning approaches for COVID-19 diagnosis using medical imaging. Symmetry, 12(9), 1526.
Montaha, S., Azam, S., Haque, A. K. M. R., Hasan, M. Z., & Karim, A. (2023). Brain tumor segmentation from 3D MRI scans using U-Net. SN Computer Science, 4, 386.
Gitonga, M. M. (2023). Multiclass MRI brain tumor segmentation using 3D attention-based U-Net. arXiv Preprint.
Yang, W., et al. (2025). A survey of U-Net variant networks for MRI brain tumor segmentation. Artificial Intelligence Review.
Saleh, M. M., Salih, M. E., Ahmed, M. A. A., & Hussein, A. M. (2025). From traditional methods to 3D U-Net: A comprehensive review of brain tumour segmentation techniques. Journal of Biomedical Science and Engineering, 18, 1–32.
Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2021/2022). nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18(2), 203–211. https://doi.org/10.1038/s41592-020-01008-z
Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Myronenko, A., Landman, B., & Xu, D. (2022). UNETR: Transformers for 3D medical image segmentation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (pp. 574–584).
Tang, Y., Yang, D., Li, W., Roth, H. R., Landman, B., Xu, D., & Nath, V. (2022). Self-supervised pre-training of Swin Transformers for 3D medical image analysis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 20730–20740).
Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., & Liang, J. (2023). UNet++: A nested U-Net architecture for medical image segmentation. IEEE Transactions on Medical Imaging, 42(1), 3–14.
Wang, G., Li, W., Ourselin, S., & Vercauteren, T. (2024). Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks in MRI images. Medical Image Analysis, 91, 102980.
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