Published August 21, 2023 | Version v1.0.1
Software Open

Pretrained model for 3D semantic image segmentation of the liver from CT scans

Creators

  • 1. BAMF Health

Description

These weights are for an nnUnet v1 model trained on TotalSegmentator and FLARE21 datasets to segment the liver from CT scans.

Notes

v1.0.0: original upload v1.0.1: removed unnecessary files, keeping only model weights

Files

Task773_Liver.zip

Files (1.2 GB)

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md5:0ff1c3aeb152bf9d416c33fe1cb67a14
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Additional details

Software

Repository URL
https://github.com/bamf-health/aimi-liver-ct

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.
  • Jakob Wasserthal. (2022). Dataset with segmentations of 104 important anatomical structures in 1204 CT images (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6802614
  • Ma, Jun. (2021). MICCAI 2021 FLARE Challenge Dataset [Data set]. In IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/TPAMI.2021.3100536