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Stereotactic radiosurgery (SRS) uses a targeted approach with less side effects than WBRT. SRS requires precise identification and delineation of BM. While artificial intelligence (AI) algorithms have been developed for this, their clinical adoption is limited due to poor model performance in the clinical setting. The limitations of algorithms are often due to the quality of datasets used for training the AI network. The purpose of this study was to create a large, heterogenous, annotated BM dataset for training and validation of AI models. We present a BM dataset of 200 patients with pretreatment T1, T1 post-contrast, T2, and FLAIR MR images. The dataset includes contrast-enhancing and necrotic 3D segmentations on T1 post-contrast and peritumoral edema 3D segmentations on FLAIR. Our dataset contains 975 contrast-enhancing lesions, many of which are sub centimeter, along with clinical and imaging information. 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K.B. \u2013 employee of Visage Imaging GmbH. W.H. \u2013 employee and stockholder of Visage Imaging GmbH. M.L. \u2013 employee and stockholder of Visage Imaging, Inc., and unrelated to this work, receives funding from NIH\/NCI R01 CA206180 and NIH\/NCI R01 CA275188. M.W. \u2013 employee and stockholder of Visage Imaging GmbH. M.S.A. \u2013 has collaborations with Visage Imaging, Inc., Blue Earth Diagnostics, Telix, and AAA. She also has a KL2 TR00186 grant from the NCATS foundation. The remaining co-authors do not have any competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"254"}}