Glioma Segmentation

Glioma segmentation, the process of automatically identifying and outlining glioma tumors in brain MRI scans, aims to improve the accuracy and efficiency of diagnosis, treatment planning, and monitoring. Current research heavily utilizes deep learning models, including U-Net variations, Vision Transformers (like MaskFormer), and ensemble methods combining multiple architectures, often incorporating radiomics features to enhance segmentation accuracy, particularly in post-treatment scenarios and with lower-quality images. These advancements hold significant promise for improving patient care by providing clinicians with more precise and readily available information about tumor characteristics, ultimately leading to better treatment outcomes and potentially reducing the need for extensive manual analysis.