mlsanityData to 7 Oct 2026

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

arXiv 2304.07248 ↗First posted 14 Apr 2023eess.IV cs.CVcited by 9

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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

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 3 of its 19 references that are in the map. First posted on arXiv 14 Apr 2023.

Builds on

3 of 19 references.

References · 3

  1. 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 507
  2. 2021Extending nn-UNet for brain tumor segmentationcited by 36
  3. 2019Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRIcited by 30

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 3; 2026: 3.

9 papers.

  1. 20233D TransUNet: Advancing Medical Image Segmentation through Vision Transformerscited by 20
  2. 2025Towards Scalable Language-Image Pre-training for 3D Medical Imagingcited by 13
  3. 20243D-TransUNet for Brain Metastases Segmentation in the BraTS2023 Challengecited by 4
  4. 2026Agentic Large Language Models for Training-Free Neuro-Radiological Image Analysiscited by 4
  5. 2023All Sizes Matter: Improving Volumetric Brain Segmentation on Small Lesionscited by 0
  6. 2025A Structured Review and Quantitative Profiling of Public Brain MRI Datasets for Foundation Model Developmentcited by 0
  7. 2025Large-Scale Pre-training Enables Multimodal AI Differentiation of Radiation Necrosis from Brain Metastasis Progression on Routine MRIcited by 0
  8. 2026U-Harmony: Enhancing Joint Training for Segmentation Models with Universal Harmonizationcited by 0
  9. 2026Improving Generalization of Deep Learning for Brain Metastases Segmentation Across Institutionscited by 0