Advancements in Brain Tumor Segmentation: A Comprehensive Review of the BraTS Challenges

Brain tumor segmentation is a critical task in medical imaging, essential for accurate diagnosis, treatment planning, and monitoring of gliomas, including high-grade and lower-grade gliomas. The Brain Tumor Segmentation (BraTS) challenges, held annually from 2017 to 2022, have significantly advanced the field by providing standardized datasets, evaluation frameworks, and fostering innovation in segmentation methodologies. In review examines the progression of BraTS challenges, highlighting key developments in data complexity, tasks, and evaluation metrics. Starting with BraTS 2017, which introduced standardized segmentation tasks and datasets with 285 subjects, subsequent iterations expanded the dataset size to 1,251 subjects by 2021 and introduced survival prediction tasks, advanced preprocessing, and enhanced segmentation algorithms. Each iteration addressed the unique challenges posed by LGG, such as diffuse boundaries and infiltrative patterns, by adopting cutting-edge architectures like 3D U-Net, nnU-Net, and Transformer-based models. These advancements have improved segmentation accuracy, robustness, and generalizability across diverse clinical settings. The review underscores the transformative role of the BraTS challenges in shaping the future of brain tumor segmentation, bridging the gap between research innovations and practical clinical applications.