A Comprehensive Review of Automatic Semantic Segmentation of Brain MRI: Techniques, Discussion, Challenges - Khan, Sajid Ullah (UMT Artificial Intelligence Review, 2025)
Brain tumors, often cancerous, require early detection to improve treatment outcomes and patient survival rates. Automated segmentation of brain tumor images using semantic parameters in medical imaging is complex due to the absence of standardized methods for diverse image dimensions. Challenges include varying image characteristics, disease severity, continuity, content, and non-uniform textures. Clinicians predominantly use labor-intensive manual segmentation. Minimizing user intervention is crucial; current algorithms are primarily semi-automatic, requiring user interaction. Fully automatic methods demand high-resolution MRI images for precise segmentation due to lower noise levels, anatomical consistency, and intensity homogeneity. This review examines traditional and advanced MRI-based segmentation techniques, highlighting challenges, advancements, and proposing future directions for integrating these methods into clinical practice to enhance diagnostic accuracy and treatment efficacy.