xmed-lab / DDAug

ICONIP 2023: Dynamic Data Augmentation via Monte-Carlo Tree Search for Prostate MRI Segmentation

Dynamic Data Augmentation via Monte-Carlo Tree Search for Prostate MRI Segmentation

This is the official implementation of ICONIP 2023: Dynamic Data Augmentation via MCTS for Prostate MRI Segmentation.

Requirements

To run the code base, first git clone the repo and install all requirements

pip install -r requirements.txt

Then navigate into DDAug/. and execute

pip install -e . 

Download Original Data

• A Multi-site Dataset for Prostate MRI Segmentation (Subset 1-6)

  • site A, B, C, D, E, F corresponds to subset 1, 2, 3, 4, 5, 6 respectively

• nnUNet Prostate MRI dataset (Subset 7)

Format Data

The folder structure follows the nnUNet folder structure. To ensure training runs without issue, you need environment variable nnUNet_raw_data_base, nnUNet_preprocessed, RESULTS_FOLDER ready. Expected folder structure is shown below.

1. Create folder for raw data and assign path to environment varialbe nnUNet_raw_data_base, in which data are expected to follow:

    nnUNet_raw_data_base/
    └── nnUNet_raw_data/
        ├── Task001_Prostate_subset1/
        │   ├── imagesTr/
        │   │   ├── some_file_name_00_0000.nii.gz
        │   │   ├── some_file_name_01_0000.nii.gz 
        │   │   ├── some_file_name_02_0000.nii.gz 
        │   │   └── ....
        │   ├── imagesTs/
        │   │   ├── some_file_name_03_0000.nii.gz
        │   │   ├── some_file_name_05_0000.nii.gz 
        │   │   ├── some_file_name_11_0000.nii.gz 
        │   │   └── ...
        │   ├── labelsTr/
        │   │   ├── some_file_name_00.nii.gz
        │   │   ├── some_file_name_01.nii.gz 
        │   │   ├── some_file_name_02.nii.gz 
        │   │   └── ....
        │   └── dataset.json
        ├── Task002_Prostate_subset2/
        │   ├── imagesTr/
        │   │   └── ...
        │   ├── imagesTs/
        │   │   └── ...
        │   ├── labelsTr/
        │   │   └── ...
        │   └── dataset.json
   

   Where the two digit _00_ in some_file_name_00_0000 indicates scan number, and the four digit at the end indicates modality number. The essential content in dataset.json include:

    {
        "name": "Prostate_RUNMC",
        "description": "",
        "reference": "",
        "licence": "",
        "release": "",
        "tensorImageSize": "4D",
        "modality": {
            "0": "MRI"
        },
        "labels": {
            "0": "background",
            "1": "PZ",
            "2": "TZ"
        },
        "numTraining": 30,
        "numTest": 0,
        "training": [
            {
                "image": "./imagesTr/RUNMC_10.nii.gz",
                "label": "./labelsTr/RUNMC_10.nii.gz"
            },
            {
                "image": "./imagesTr/RUNMC_06.nii.gz",
                "label": "./labelsTr/RUNMC_06.nii.gz"
            },
            ...
        ],
        "test": [],
        "testing": []
    }
   

   please note due to limited data size and as described in the paper, we reported mean DICE of 5-fold cross validation on the validation set using the weights of the last epoch.

2. After formatting all the raw data, create folder for processed data and assign to environment variable nnUNet_preprocessed, then run the command

   nnUNet_plan_and_preprocess -t Task001_Prostate_subset1 

   nnUNet will then create processed data in

    nnUNet_preprocessed/
    ├── Task001_Prostate_subset1/
    │   └── ....
    ├── Task002_Prostate_subset2/
    │   └── ....
   

3. Finally you need to create folder for training result and assign to environment variable RESULTS_FOLDER. Train logs, weights will be stored there.

Training

To run 5-fold cross validation, make sure all environment variables are set, and execute

for fold in 0 1 2 3 4; do CUDA_VISIBLE_DEVICES=1 nnUNet_train 3d_fullres nnUNetTrainerV2_MCTS Task001_Prostate_subset1 $fold --npz; done;

Validation

Once training completes, with the same environment variables, execute

python nnunet/inference/summarize_val_folds.py

This will generate inference result and csv file with mean DICE score with weights using all 5-fold trainings. Please note the option --disable_tta is set to True in file nnunet/inference/summarize_val_folds.py.

Result Summarization and Visualization

You can use below code in jupyter notebook to have a nice visualization of all the results. (the for-else loop is not a bug)

result_folder = "RESULTS_FOLDER/nnUNet/3d_fullres"

for each_task in sorted(os.listdir(result_folder)):
    print("-" * 100)
    task_dir = f"{result_folder}/{each_task}"
    for each_model in sorted(os.listdir(task_dir)):
        model_dir = f"{task_dir}/{each_model}"

        table = pd.DataFrame()
        normal_exit = False
        for fold in range(5):
            each_fold = f"fold_{fold}"
            if not os.path.isfile(f"{model_dir}/{each_fold}/testing/result.csv"):
                continue
            fold_result = pd.read_csv(f"{model_dir}/{each_fold}/testing/result.csv", index_col=0).drop(
                index=["mean", "std"]
            )
            table = pd.concat([table, fold_result])
        else:
            normal_exit = True
            # normal exit
            mean_all = pd.DataFrame(
                data=[table.mean(axis=0).to_numpy()], columns=table.columns, index=["mean"]
            )
            std_all = pd.DataFrame(data=[table.std(axis=0).to_numpy()], columns=table.columns, index=["std"])
            table = pd.concat([table, mean_all, std_all])
            table.to_csv(f"{model_dir}/fold_summary.csv")
            print(f'out csv [{table.shape}] -> {each_task} {each_model.split("__")[0]}')
            print(mean_all.to_string())

        if not normal_exit:
            print(f'failed with -> {each_task} {each_model.split("__")[0]}')
        print("\n-----\n")

You can use the draw_circle_using_mask.py to generate below images. Modify line 48, 60 and 61 for filename and folder paths.

Acknowledgement

Citation