AI-Driven Brain Tumor Segmentation: Innovations, Constraints and the Path Toward Clinical Adoption

Brain tumor segmentation is a challenging and critical task in the area of medical imaging and treatment planning. The result of the segmentation process is to generate an accurate sketch of the tumor regions by their corresponding brain tumor masks. The precise and accurate segmentation of tumors requires multiple image modalities with varying contrasts and spatial resolution. In recent years, many deep learning models such as U-Net, ResNet, VGG16, and Transformer-based architectures have emerged and achieved promising results with exceptional breakthroughs. The survey has been utilized to review stateof-the-art deep learning methods for brain tumor segmentation, clearly focusing on their approach and various strategies. Existing studies primarily use benchmark datasets like BraTS, TCIA, and Kaggle MRI datasets for training and evaluation. The survey has also discussed the variety of challenges faced in the segmentation process, including performance metrics like Dice score, sensitivity, and specificity. The study outlines a clear pathway for several future directions in the area of brain tumor segmentation.