mousechd 0.0.6

Screening of Congenital Heart Diseases (CHD) in mice with 3D CTscans.

Napari plugin: MouseCHD Napari plugin

Installation

There are three ways that you can run the package:

Conda environment

• Create virtual environment: conda create -n mousechd python=3.9
• Activate the environment: conda activate mousechd
• Install the package: pip install mousechd

Docker

• Pull the docker image: sudo docker pull hoanguyen93/mousechd
• Test if docker image pulled successfully: sudo docker run mousechd mousechd -h

usage: mousechd [-h] [-version] {postprocess_nnUNet,prepare_nnUNet_data,preprocess,segment,resample,split_data,viz3d_views,viz3d_stages,viz_stacks,viz_eda,viz3d_seg,create_label_df,test_clf,train_clf,explain,viz_grad} ...

optional arguments:
  -h, --help            show this help message and exit
  -version              show program's version number and exit

Choose a command:
  {postprocess_nnUNet,prepare_nnUNet_data,preprocess,segment,resample,split_data,viz3d_views,viz3d_stages,viz_stacks,viz_eda,viz3d_seg,create_label_df,test_clf,train_clf,explain,viz_grad}

To assure that you can run the docker with GPUs if available, see Running docker with GPU section.

Apptainer

In case you run the package on HPC on which you don't have superuser permission, you can use Apptainer instead of docker.

• Download container to your computer or HPC:

wget https://zenodo.org/records/13855119/files/mousechd.sif

• On HPC, the internet connection may not be not available on running node, you should download models in advance. See the downloading instruction Downloading models in advance on HPC
• Test if container run correctly: apptainer exec --nv <path/to/mousechd.sif> mousechd -h

usage: mousechd [-h] [-version] {postprocess_nnUNet,prepare_nnUNet_data,preprocess,segment,resample,split_data,viz3d_views,viz3d_stages,viz_stacks,viz_eda,viz3d_seg,create_label_df,test_clf,train_clf,explain,viz_grad} ...

optional arguments:
  -h, --help            show this help message and exit
  -version              show program's version number and exit

Choose a command:
  {postprocess_nnUNet,prepare_nnUNet_data,preprocess,segment,resample,split_data,viz3d_views,viz3d_stages,viz_stacks,viz_eda,viz3d_seg,create_label_df,test_clf,train_clf,explain,viz_grad}

How to use

It is recommended that your data are structured in the following way:

    DATABASE # your database name
    └── raw # raw folder to store raw data
        ├── NameOfDataset1 # name of dataset
        │   ├── images_20200206 # folder to store images recieved on 20200206 [YYYYMMDD]
        │   ├── masks_20210115 # folder to store masks recieved on 20210115 [YYYYMMDD]
        │   ├── masks_20210708 # folder to store masks recieved on 20210708 [YYYYMMDD]
        │   └── metadata_20210703.csv # metadata file received on 20210703 [YYYYMMDD]
        └── NameOfDataset2 # name of another dataset
            └── images_20201010
            ......

In case you use container, see Running mousechd with docker and Running mousechd with Apptainer for more details.

(1) Preprocessing

This step standardizes the data into the same spacing and view.

• Data format supported: "DICOM", "NRRD", "NIFTI"
• Mask data format supported: "TIF2d", "TIF3d", "NIFTI"

mousechd preprocess \
    -database <PATH/TO/DATABASE> \
    -imdir <PATH/TO/IMAGE/DIR> \ # relative to databse
    -maskdir <PATH/TO/MASK/DIR> \ # relative to database
    -masktype NIFTI \
    -metafile <PATH/TO/META/FILE> \ # csv file with headers: "heart_name", "Stage", "Normal heart", "CHD1", "CHD2", ...
    -outdir "DATA/processed"

(2) Heart segmentation

mousechd segment -indir "DATA/processed/images" -outdir "OUTPUTS/HeartSeg"

If your computer crashes when running this, you can decrease the number of threads for preprocessing (-num_threads_preprocessing, default: 6) and saving NIFTI files (-num_thread_nifti_save, default: 2)

(3) CHD detection

mousechd test_clf \
    -imdir "DATA/processed/images" \
    -maskdir  "OUTPUTS/HeartSeg" \
    -stage ["eval"|"test"] \
    -label [PATH/TO/CSV/TEST/FILE] \ # <optional> if stage is "eval", -label must be specified
    -outdir [PATH/TO/OUTPUT/DIRECTORY]

Retraining

You have the option to retrain the model using your custom dataset. After completing the heart segmentation, resample to augment the data, followed by data splitting and subsequence model retraining.

mousechd resample \
    -imdir  "DATA/processed/images" \
    -maskdir  "OUTPUTS/HeartSeg" \
    -outdir "DATA/resampled" \
    -metafile  "DATA/processed/metadata.csv" \
    -save_images 1

mousechd split_data \
    -metafile "DATA/processed/metadata.csv" \
    -outdir "DATA/label" \
    -val_size 0.2

mousechd train_clf \
    -exp_dir "OUTPUTS/Classifier" \
    -exp [EXPERIEMENT_NAME] \
    -data_dir "DATA/resampled" \
    -label_dir "DATA/label/x5_base/1fold" \
    -epochs [NUM_EPOCHS]

mousechd test_clf \
    -model_dir "OUTPUTS/Classifier/<EXPERIMENT_NAME>" \
    -imdir "DATA/processed/images" \
    -maskdir  "OUTPUTS/HeartSeg" \
    -stage ["eval"|"test"] \
    -label [PATH/TO/CSV/TEST/FILE] \ # <optional> if stage is "eval", -label must be specified
    -outdir [PATH/TO/OUTPUT/DIRECTORY]

GradCAM

mousechd explain \
-exp_dir "OUTPUTS/Classifier/<EXPERIMENT_NAME>" \
-imdir "DATA/resampled/images" \
-outdir [PATH/TO/OUTPUT/DIRECTORY]

Analysis

A detailed analysis can be found in the folder analysis.

For some visualization, Napari is required. To install: pip install "napari[all]".

Acknowledgements

• INCEPTION funding: INCEPTION
• GPU server technical support: Quang Tru Huynh