Abstract
The BraTioUS (Brain Tumor Intraoperative Ultrasound) dataset [1] is a large-scale, multicenter, and publicly available collection of intraoperative ultrasound (ioUS) images acquired during glioma surgeries. Created through an international collaboration among six hospitals across five countries, BraTioUS comprises 1669 B-mode 2D ioUS images from 142 glioma patients collected between 2018 and 2023 using various ultrasound systems and acquisition protocols. It also includes masks supervised by experts of tumor segmentation from every ioUS image. BraTioUS addresses several limitations found in existing public datasets, such as lack of diversity in acquisition hardware, imaging protocols, and glioma types. The primary objective of this dataset is to be publicly available and accessible for the training and validation of machine learning models aimed at improving the interpretation and use of ioUS. The dataset's scale, quality, and heterogeneity make it a valuable resource for training and validating AI tools aimed at improving intraoperative decision-making and patient outcomes in glioma surgery.