nnunet_baseline

How to install

Using Python 3.9
cd nnUNet-baseline
pip install -e .

How to train a model

1.Preparing the dataset：

Datasets must be located in the nnUNet_raw folder (which you either define when installing nnU-Net or export/set every time you intend to run nnU-Net commands!). Each segmentation dataset is stored as a separate 'Dataset'. Datasets are associated with a dataset ID, a three digit integer, and a dataset name (which you can freely choose): For example, Dataset001_autopet_501 has 'autopet_501' as dataset name and the dataset id is 1. Datasets are stored in the nnUNet_raw folder。
Exactly the same as nnunet_v2, please refer to: https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/

2.Importing environment variables

Create three folders: nnUNet_preprocessed (to store processed data), nnUNet_trained_models (to store trained models),
and nnUNet_raw (to store the dataset).

export nnUNet_preprocessed="nnUNet_preprocessed_directory"
export nnUNet_results="nnUNet_trained_models_directory"
export nnUNet_raw="nnUNet_raw_directory" In this competition, the model is located in the following folder:nnUNet_trained_models/Dataset001_autopet_501

3.The training command

nnUNetv2_plan_and_preprocess -d 1 -c 3d_fullres -np 8 --verify_dataset_integrity && nnUNetv2_train 1 3d_fullres all.
Where 1 is the task ID number。

How to inference

nnUNetv2_predict -i imagesTs -o infer -d 1 -c 3d_fullres -f all
Where 1 is the task ID number. 'imagesTs' stands for test data, and 'infer' is the inference folder.