Published June 11, 2024 | Version v1.0.0
Software Open

Pretrained model for 3D semantic image segmentation of the brain tumor, necrosis, and edema from MRI scans

Creators

  • 1. BAMF Health

Description

These weights are for an nnU-Net v2 model that segments brain tumors, necrosis, and edema from MRI scans. This model was trained on the BRATS 2021 dataset.

Files

Dataset002_BRATS19.zip

Files (1.2 GB)

Name Size Download all
md5:23a3f55dead4a6642271a08d1a503bbb
1.2 GB Preview Download

Additional details

References

  • Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
  • U.Baid, et al., "The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification", arXiv:2107.02314, 2021
  • B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, et al. "The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)", IEEE Transactions on Medical Imaging 34(10), 1993-2024 (2015) DOI: 10.1109/TMI.2014.2377694
  • S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J.S. Kirby, et al., "Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features", Nature Scientific Data, 4:170117 (2017) DOI: 10.1038/sdata.2017.117