AI-Driven Brain Tumor Segmentation: Review of the Last Decade

Abstract

Brain tumors remain among the most difficult of the medical challenges, and the accurate and timely diagnosis is essential to achieve successful patient outcomes. Over the last decade, artificial intelligence (AI), and specifically deep learning, has profoundly transformed the paradigms of brain tumor detection and segmentation methodologies. This comprehensive review systematically examines the evolution of brain tumor segmentation AI models between 2015 and 2025, covering technological advancements, performance evaluation techniques, and challenges towards clinical translation. We follow the evolution from traditional machine learning approaches to sophisticated deep learning architectures, including Convolutional Neural Networks (CNNs), U-Net architectures, and the more recently emerged Vision Transformers (ViTs). It takes into account the most crucial performance metrics, i.e., Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), accuracy, sensitivity, and specificity, which are primarily tested against benchmarking datasets, such as BraTS. Our findings register noteworthy improvements in performance, wherein the top-performing current ensemble and transformer-based models deliver Dice scores well above 0.95 for whole-tumor segmentation. Despite the stunning progress, limitations in standardization of evaluation, model generalizability across clinical settings and interpretability persist. This review describes the critical views of current capabilities, shortcomings, and directions of AI-based brain tumor segmentation systems with focus on the road to strong clinical deployment of these systems.