# The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) MRI Dataset

arXiv: https://arxiv.org/abs/2304.07248 · HTML: https://mlsanity.com/paper/2304.07248/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 14 Apr 2023.

## Builds on (most cited first, 3 of 3)

- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)
- [Extending nn-UNet for brain tumor segmentation](https://mlsanity.com/paper/2112.04653/) (2021)
- [Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRI](https://mlsanity.com/paper/1903.07988/) (2019)

## Built on by (most cited first, 9 of 9)

- [3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers](https://mlsanity.com/paper/2310.07781/) (2023)
- [Towards Scalable Language-Image Pre-training for 3D Medical Imaging](https://mlsanity.com/paper/2505.21862/) (2025)
- [3D-TransUNet for Brain Metastases Segmentation in the BraTS2023 Challenge](https://mlsanity.com/paper/2403.15735/) (2024)
- [Agentic Large Language Models for Training-Free Neuro-Radiological Image Analysis](https://mlsanity.com/paper/2604.16729/) (2026)
- [A Structured Review and Quantitative Profiling of Public Brain MRI Datasets for Foundation Model Development](https://mlsanity.com/paper/2510.20196/) (2025)
- [All Sizes Matter: Improving Volumetric Brain Segmentation on Small Lesions](https://mlsanity.com/paper/2310.02829/) (2023)
- [Large-Scale Pre-training Enables Multimodal AI Differentiation of Radiation Necrosis from Brain Metastasis Progression on Routine MRI](https://mlsanity.com/paper/2511.18208/) (2025)
- [U-Harmony: Enhancing Joint Training for Segmentation Models with Universal Harmonization](https://mlsanity.com/paper/2601.14605/) (2026)
- [Improving Generalization of Deep Learning for Brain Metastases Segmentation Across Institutions](https://mlsanity.com/paper/2604.00397/) (2026)

## Abstract (arXiv)

The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) dataset is a public, clinical, multimodal brain MRI dataset consisting of 560 brain MRIs from 412 patients with expert annotations of 5136 brain metastases. Data consists of registered and skull stripped T1 post-contrast, T1 pre-contrast, FLAIR and subtraction (T1 pre-contrast - T1 post-contrast) images and voxelwise segmentations of enhancing brain metastases in NifTI format. The dataset also includes patient demographics, surgical status and primary cancer types. The UCSF-BSMR has been made publicly available in the hopes that researchers will use these data to push the boundaries of AI applications for brain metastases. The dataset is freely available for non-commercial use at https://imagingdatasets.ucsf.edu/dataset/1