  Gliomas are the most common malignant primary brain tumors in adults and one
of the deadliest types of cancer. There are many challenges in treatment and
monitoring due to the genetic diversity and high intrinsic heterogeneity in
appearance, shape, histology, and treatment response. Treatments include
surgery, radiation, and systemic therapies, with magnetic resonance imaging
(MRI) playing a key role in treatment planning and post-treatment longitudinal
assessment. The 2024 Brain Tumor Segmentation (BraTS) challenge on
post-treatment glioma MRI will provide a community standard and benchmark for
state-of-the-art automated segmentation models based on the largest
expert-annotated post-treatment glioma MRI dataset. Challenge competitors will
develop automated segmentation models to predict four distinct tumor
sub-regions consisting of enhancing tissue (ET), surrounding non-enhancing
T2/fluid-attenuated inversion recovery (FLAIR) hyperintensity (SNFH),
non-enhancing tumor core (NETC), and resection cavity (RC). Models will be
evaluated on separate validation and test datasets using standardized
performance metrics utilized across the BraTS 2024 cluster of challenges,
including lesion-wise Dice Similarity Coefficient and Hausdorff Distance.
Models developed during this challenge will advance the field of automated MRI
segmentation and contribute to their integration into clinical practice,
ultimately enhancing patient care.
