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Example: 3D U-Net training on the Hippocampus dataset

This is a step-by-step example on how to run a 3D full resolution Training with the Hippocampus dataset from the Medical Segmentation Decathlon.

  1. Install nnU-Net by following the instructions here. Make sure to set all relevant paths, also see here. This step is necessary so that nnU-Net knows where to store raw data, preprocessed data and trained models.

  2. Download the Hippocampus dataset of the Medical Segmentation Decathlon from here. Then extract the archive to a destination of your choice.

  3. Decathlon data come as 4D niftis. This is not compatible with nnU-Net (see dataset format specified here). Convert the Hippocampus dataset into the correct format with

    nnUNet_convert_decathlon_task -i /xxx/Task04_Hippocampus

    Note that Task04_Hippocampus must be the folder that has the three 'imagesTr', 'labelsTr', 'imagesTs' subfolders! The converted dataset can be found in $nnUNet_raw_data_base/nnUNet_raw_data ($nnUNet_raw_data_base is the folder for raw data that you specified during installation)

  4. You can now run nnU-Nets pipeline configuration (and the preprocessing) with the following line:

    nnUNet_plan_and_preprocess -t 4

    Where 4 refers to the task ID of the Hippocampus dataset.

  5. Now you can already start network training. This is how you train a 3d full resoltion U-Net on the Hippocampus dataset:

    nnUNet_train 3d_fullres nnUNetTrainerV2 4 0

    nnU-Net per default requires all trainings as 5-fold cross validation. The command above will run only the training for the first fold (fold 0). 4 is the task identifier of the hippocampus dataset. Training one fold should take about 20 hours on a modern GPU.

This tutorial is only intended to demonstrate how easy it is to get nnU-Net running. You do not need to finish the network training - pretrained models for the hippocampus task are available (see here).

The only prerequisite for running nnU-Net on your custom dataset is to bring it into a structured, nnU-Net compatible format. nnU-Net will take care of the rest. See here for instructions on how to convert datasets into nnU-Net compatible format.