A Review of Deep Learning and Artificial Intelligence Approaches for Brain Tumor Segmentation Using 3D MRI Data
Keywords:
brain tumor segmentation; deep learning; 3D MRI; convolutional neural networks; U-Net; transformers; BraTS; medical image analysisAbstract
Brain tumor segmentation from three-dimensional (3D) magnetic resonance imaging (MRI) is a cornerstone task in computational neuro-oncology, supporting diagnosis, surgical planning, radiotherapy targeting, and longitudinal treatment monitoring. Manual delineation of tumor sub-regions by expert radiologists is accurate but time-consuming, subject to inter- and intra-observer variability, and impractical at scale. Over the past decade, artificial intelligence (AI), and in particular deep learning, has transformed the field by enabling automated, reproducible, and increasingly accurate segmentation of heterogeneous tumor tissue across multi-modal MRI sequences. This review surveys the evolution of deep learning and AI approaches for brain tumor segmentation using volumetric MRI data, with emphasis on convolutional neural network (CNN) architectures, the influential U-Net family and its 3D extensions, encoder-decoder designs, attention mechanisms, and the recent emergence of transformer-based and hybrid models. We examine the role of benchmark datasets, most notably the Brain Tumor Segmentation (BraTS) challenge, in driving methodological progress, and we discuss preprocessing, data augmentation, loss function design, and evaluation metrics such as the Dice similarity coefficient and Hausdorff distance. The review further considers persistent challenges including class imbalance, domain shift across scanners and institutions, limited annotated data, high computational cost of volumetric processing, and the clinical need for uncertainty quantification and interpretability. We organize the literature into four thematic strands: CNN-based volumetric architectures, the U-Net family and encoder-decoder refinements, attention and transformer-based methods, and approaches addressing data efficiency and generalization. We conclude that while deep learning has achieved expert-level performance on curated benchmarks, robust clinical translation requires advances in generalization, trust, computational efficiency, and integration into clinical workflows.
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