Brain Tumor Segmentation

Brain tumor segmentation involves automatically identifying and outlining tumor regions in medical images, primarily MRI scans, to aid in diagnosis and treatment planning. Current research focuses on improving segmentation accuracy and robustness using advanced deep learning architectures like U-Net and its variants (e.g., Swin UNETR, nnU-Net), often incorporating attention mechanisms and multi-scale feature extraction to better handle the complex heterogeneity of brain tumors. These advancements are crucial for improving the speed and accuracy of clinical diagnosis, facilitating personalized treatment strategies, and potentially leading to better patient outcomes. Furthermore, significant effort is dedicated to addressing challenges like missing modalities and imbalanced datasets.