Published June 18, 2024 | Version v2
Model Open

Pretrained model for 3D semantic image segmentation of the Breast and Fibroglandular Tissue from MRI scans

Creators

  • 1. BAMF Health

Description

These weights are for an nnUnet v2 model that segments Breast and Fibroglandular tissue (FGT) from mri scans. The model was trained on DUKE Breast Cancer MRI dataset and ISPY1 Tumor SEG Radiomics dataset from TCIA

Files

Dataset009_Breast.zip

Files (2.3 GB)

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Additional details

Dates

Created
2024-06-06

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.
  • Newitt, D., & Hylton, N. (2016). Single site breast DCE-MRI data and segmentations from patients undergoing neoadjuvant chemotherapy (Version 3) [Data set]. The Cancer Imaging Archive
  • Chitalia, R., Pati, S., Bhalerao, M., Thakur, S., Jahani, N., Belenky, J. V., McDonald, E.S., Gibbs, J., Newitt, D., Hylton, N., Kontos, D., & Bakas, S. (2021). Expert tumor annotations and radiomic features for the ISPY1/ACRIN 6657 trial data collection [Data set]. The Cancer Imaging Archive