Optimizing Magnetic Resonance Image Segmentation Through Scalable Deep Learning and Hierarchical Data Management
Automated glioma segmentation in multi-modal magnetic resonance imaging (MRI) is critical in clinical neurooncology, yet it is challenged by large data volumes, tumor heterogeneity, and class imbalance. This study proposes an efficient and scalable system based on an optimized 2D U-Net architecture, integrated within a...