Final submission Singularity container for the winning contribution of the Multi-Centre, Multi-Vendor & Multi-Disease Cardiac Image Segmentation Challenge (M&Ms). The model inside container was trained with the nnU-Net framework. In order to be able to run this container you will need a GPU comparable to TITAN Xp (12 GB VRAM) or larger. Follow these steps to perform fully automatic segmentation on short axis (SAX) cMRI data: install Singularity: Follow instructions by sylabs. prepare your data as 3D .nii.gz files put your data in a folder, that will serve as your input folder (<input_folder>) to make your folder structure conform with the folder structure of the challenge place a subfolder called "mnms" inside <input_folder> as shown below <input_folder> |- subfolder |-- image01.nii.gz |-- image02.nii.gz |-- ... Then run the container singularity run --nv <path_to_container_folder>/MIC_DKFZ_mnms_final_submission.sif <input_folder> <output_folder> The nnU-Net inside the container will automatically ensemble five 2D and five 3D pretrained U-Nets and will save the final prediction in your defined <output_folder>.

{"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."]}