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

```bash
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:
bash 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:
bash 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 9
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.