To install requirements:
git clone https://github.com/luckieucas/FLARE23.git
cd nnUNet
pip install -e .
Running the data preprocessing code:
nnUNetv2_plan_and_preprocess -d 12 --verify_dataset_integrity
python run_training_Flare.py 12 3d_mylowres 1 -tr nnUNetTrainerFlarePseudoCutUnsupLow -p nnUNetPlans
nnUNetv2_predict -i <path_to_data> -o <path_to_output_data> -d 12 -c 3d_mylowres -f 1 -chk <name_of_trained_model> -tr nnUNetTrainerFlarePseudoCutUnsupLow -step_size 0.6 -npp 3 --disable_tta
To compute the evaluation metrics, run:
nnUNetv2_evaluate_folder <path_to_ground_truth> <path_to_inference_results>
Our method achieves the following performance on MICCAI FLARE23: Fast, Low-resource, and Accurate oRgan and Pan-cancer sEgmentation in Abdomen CT
Model name | Organ DICE(%) | Organ NSD(%) | Tumor DICE(%) | Tumor NSD(%) |
---|---|---|---|---|
Our method | 92.18 | 96.33 | 46.26 | 38.65 |
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We thank the contributors of public datasets.