BraTS Generalizability Across Tumors

The International Brain Tumor Segmentation (BraTS) challenge has been focusing, since its inception in 2012, on the generation of a benchmarking environment and a dataset for the delineation of adult brain gliomas. The focus of BraTS2023 challenge remained the same in terms of generating the common benchmark environment, while the dataset expands into explicitly addressing 1) the same adult glioma population, as well as 2) the underserved sub-Saharan African brain glioma patient population, 3) brain/intracranial meningioma, 4) brain metastasis, and 5) pediatric brain tumor patients. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. That is, each segmentation method was evaluated exclusively on the patients population it was trained on in each sub-challenge. In this challenge, we aim to organize the Generalizability Assessment of Segmentation Algorithms Across Brain Tumors. The hypothesis is that a method capable of performing well on multiple segmentation tasks will generalize well on unseen tasks. Specifically, in this task, we will be focusing on assessing the algorithmic generalizability beyond each individual patient population and focus across all of them. Importantly, although each MR exams will undergo the same preprocessing pipeline, including an intensity normalization step, there are characteristics of each exam that will not be affected (I.e., different number of lesions per exam, different location within the brain, etc.) preserving the generalizability aspect of the challenge.