Published October 26, 2020 | Version v4
Software Open

Pretrained nnU-Net Model from the cMRI M&Ms Challenge 2020

Authors/Creators

  • 1. Division of Medical Image Computing, German Cancer Research Center (DKFZ); Heidelberg University, Medical Faculty Heidelberg, Heidelberg, Germany)
  • 2. Division of Medical Image Computing, German Cancer Research Center (DKFZ)

Description

Pretrained model of the winning contribution of the "Multi-Centre, Multi-Vendor & Multi-Disease Cardiac Image Segmentation Challenge (M&Ms)".

The model is build with the nnU-Net framework and comprises an ensemble of five 2d and five 3d models.

The model can be downloaded (after setting up the nnU-Net framework) using following command

nnUNet_download_pretrained_model Task114_heart_MNMs

Data needs to be available as 3D .nii.gz files. For further specification how to prepare your data see or follow this nnU-Net example on prostate MRI data

Once your data is prepared you can run inference with

# run prediction with 2d nnU-Net
nnUNet_predict -i <path_to_input_folder> -o <path_to_temporary_output_folder_2d> --save_npz -t 114 -m 2d -tr nnUNetTrainerV2_MMS

# run prediction with 3d nnU-Net
nnUNet_predict -i <path_to_input_folder> -o <path_to_temporary_output_folder_3d> --save_npz -t 114 -m 3d_fullres -tr nnUNetTrainerV2_MMS

# ensemble 2d and 3d predictions
nnUNet_ensemble -f <path_to_temporary_output_folder_2d> <path_to_temporary_output_folder_3d> -o <path_to_final_predictions>

In case you find these models useful for your research, cite the original work, please:

Full P.M., Isensee F., Jäger P.F., Maier-Hein K. (2021) Studying Robustness of Semantic Segmentation Under Domain Shift in Cardiac MRI. In: Puyol Anton E. et al. (eds) Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges. STACOM 2020. Lecture Notes in Computer Science, vol 12592. Springer, Cham. https://doi.org/10.1007/978-3-030-68107-4_24

Files

Task114_heart_MNMs.zip

Files (2.3 GB)

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md5:1bc89143f70fe5f8236cef359518516a
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Additional details

References

  • F. Isensee, P. Jaeger, S. Kohl, J. Petersen, and K. Maier-Hein, "nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation," Nature Methods, vol. 18, pp. 1–9, Feb. 2021, doi: 10.1038/s41592-020-01008-z.
  • Peter M. Full, Fabian Isensee, Paul F. Jäger, Klaus Maier-Hein "Studying Robustness of Semantic Segmentation under Domain Shift in cardiac MRI" arXiv preprint arXiv:2011.07592 (2020).
  • V. M. Campello et al., "Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge," IEEE Transactions on Medical Imaging, vol. PP, pp. 1–1, Jun. 2021, doi: 10.1109/TMI.2021.3090082.