3D Brain Tumor

3D brain tumor segmentation from MRI scans is a crucial area of research aiming to automate the precise delineation of tumors for improved diagnosis and treatment planning. Current efforts focus on developing efficient and accurate deep learning models, including variations of U-Net architectures, transformer-based networks, and generative adversarial networks (GANs), often incorporating attention mechanisms and multi-scale feature extraction to handle the complex heterogeneity of brain tumors. These advancements strive to improve segmentation accuracy, reduce computational demands for resource-constrained settings, and enhance the interpretability of model predictions. Ultimately, improved 3D brain tumor segmentation promises to accelerate clinical workflows and lead to more effective personalized cancer care.