mousechd 0.0.3a0

Segmenting hearts and screening congenital heart diseases in mice

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

Napari plugin: MouseCHD Napari plugin

Installation

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

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
            ......

(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.py \
    -database <PATH/TO/DATABASE> \
    -maskdir <PATH/TO/MASK/DIR> \
    -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"

(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.

Click here to expand the instruction

(1) Resample

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

(2) Split data

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

(3) Train

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

(4) Evaluate retrained model

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