Screening of Congenital Heart Diseases (CHD) in mice with 3D CTscans.
Napari plugin: MouseCHD Napari plugin
There are three ways that you can run the package:
- Create virtual environment:
conda create -n mousechd python=3.9 - Activate the environment:
conda activate mousechd - Install the package:
pip install mousechd
- Pull the docker image:
sudo docker pull hoanguyen93/mousechd - Test if docker image pulled successfully:
sudo docker run mousechd mousechd -h
Expected output:
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.
In case you run the package on 8000 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/13928753/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
Expected output:
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}
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.
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"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)
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]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.
Click here to expand the instruction
mousechd resample \
-imdir "DATA/processed/images" \
-maskdir "OUTPUTS/HeartSeg" \
-outdir "DATA/resampled" \
-metafile "DATA/processed/metadata.csv" \
-save_images 1mousechd split_data \
-metafile "DATA/processed/metadata.csv" \
-outdir "DATA/label" \
-val_size 0.2mousechd 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]mousechd explain \
-exp_dir "OUTPUTS/Classifier/<EXPERIMENT_NAME>" \
-imdir "DATA/resampled/images" \
-outdir [PATH/TO/OUTPUT/DIRECTORY]A detailed analysis can be found in the folder analysis.
For some visualization, Napari is required. To install: pip install "napari[all]".
- INCEPTION funding: INCEPTION
- GPU server technical support: Quang Tru Huynh