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> Read the original work at 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

{"references": ["Fabian Isensee, Paul F. J\u00e4ger, Simon A. A. Kohl, Jens Petersen, Klaus H. Maier-Hein \"Automated Design of Deep Learning Methods for Biomedical Image Segmentation\" arXiv preprint arXiv:1904.08128 (2020).", "Campello, V\u00edctor M. et al.: Multi-Centre, Multi-Vendor & Multi-Disease Cardiac Image Segmentation. In preparation."]}