Published October 26, 2020 | Version 1.0
Software Open

nnU-Net Singularity Container for SAX cMRI Segmentation Trained on Data from the M&Ms Challenge 2020

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

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:

  1. install Singularity: Follow instructions by sylabs.
  2. prepare your data as 3D .nii.gz files
  3. put your data in a folder, that will serve as your input folder (<input_folder>)
  4. 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>.

Files

Files (4.1 GB)

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md5:0acbb80eaac1399e5c71ee99bcef8c03
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Additional details

References

  • Fabian Isensee, Paul F. Jäger, 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, Víctor M. et al.: Multi-Centre, Multi-Vendor & Multi-Disease Cardiac Image Segmentation. In preparation.