Screening of Congenital Heart Diseases (CHD) in mice with 3D <img src="https://latex.codecogs.com/svg.latex?\mu" /> CTscans.
***Napari plugin: [MouseCHD Napari plugin](https://github.com/hnguyentt/mousechd-napari)***
## 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"
```bash
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
```bash
mousechd segment -indir "DATA/processed/images" -outdir "OUTPUTS/HeartSeg"
```
### (3) CHD detection
```bash
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.
<details>
<summary>Click here to expand the instruction</summary>
### (1) Resample
```bash
mousechd resample \
-imdir "DATA/processed/images" \
-maskdir "OUTPUTS/HeartSeg" \
-outdir "DATA/resampled" \
-metafile "DATA/processed/metadata.csv" \
-save_images 1
```
### (2) Split data
```bash
mousechd split_data \
-metafile "DATA/processed/metadata.csv" \
-outdir "DATA/label" \
-val_size 0.2
```
### (3) Train
```bash
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
```bash
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]
```
</details>
## GradCAM
```bash
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](./analysis/).
For some visualization, [Napari](https://napari.org/stable/) is required. To install: `pip install "napari[all]`.
## Acknowledgements
* INCEPTION funding: [INCEPTION](https://www.inception-program.fr/en)
* GPU server technical support: Quang Tru Huynh
Raw data
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"description": "\n\nScreening of Congenital Heart Diseases (CHD) in mice with 3D <img src=\"https://latex.codecogs.com/svg.latex?\\mu\" /> CTscans.\n\n***Napari plugin: [MouseCHD Napari plugin](https://github.com/hnguyentt/mousechd-napari)***\n\n\n## Installation\n* Create virtual environment: `conda create -n mousechd python=3.9`\n* Activate the environment: `conda activate mousechd`\n* Install the package: `pip install mousechd`\n \n## How to use\n\nIt is recommended that your data are structured in the following way:\n```\n DATABASE # your database name\n \u2514\u2500\u2500 raw # raw folder to store raw data\n \u251c\u2500\u2500 NameOfDataset1 # name of dataset\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 images_20200206 # folder to store images recieved on 20200206 [YYYYMMDD]\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 masks_20210115 # folder to store masks recieved on 20210115 [YYYYMMDD]\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 masks_20210708 # folder to store masks recieved on 20210708 [YYYYMMDD]\n \u2502\u00a0\u00a0 \u2514\u2500\u2500 metadata_20210703.csv # metadata file received on 20210703 [YYYYMMDD]\n \u2514\u2500\u2500 NameOfDataset2 # name of another dataset\n \u2514\u2500\u2500 images_20201010\n ......\n```\n\n### (1) Preprocessing\n\nThis step standardizes the data into the same spacing and view.\n* Data format supported: \"DICOM\", \"NRRD\", \"NIFTI\"\n* Mask data format supported: \"TIF2d\", \"TIF3d\", \"NIFTI\"\n\n```bash\nmousechd preprocess.py \\\n -database <PATH/TO/DATABASE> \\\n -maskdir <PATH/TO/MASK/DIR> \\\n -masktype NIFTI \\\n -metafile <PATH/TO/META/FILE> \\ # csv file with headers: \"heart_name\", \"Stage\", \"Normal heart\", \"CHD1\", \"CHD2\", ...\n -outdir \"DATA/processed\"\n```\n\n### (2) Heart segmentation\n\n ```bash\n mousechd segment -indir \"DATA/processed/images\" -outdir \"OUTPUTS/HeartSeg\"\n ```\n\n### (3) CHD detection\n```bash\nmousechd test_clf \\\n -imdir \"DATA/processed/images\" \\\n -maskdir \"OUTPUTS/HeartSeg\" \\\n -stage [\"eval\"|\"test\"] \\\n -label [PATH/TO/CSV/TEST/FILE] \\ # <optional> if stage is \"eval\", -label must be specified\n -outdir [PATH/TO/OUTPUT/DIRECTORY]\n```\n\n## Retraining\n\nYou 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.\n\n<details>\n<summary>Click here to expand the instruction</summary>\n\n### (1) Resample\n```bash\nmousechd resample \\\n -imdir \"DATA/processed/images\" \\\n -maskdir \"OUTPUTS/HeartSeg\" \\\n -outdir \"DATA/resampled\" \\\n -metafile \"DATA/processed/metadata.csv\" \\\n -save_images 1\n```\n\n### (2) Split data\n```bash\nmousechd split_data \\\n -metafile \"DATA/processed/metadata.csv\" \\\n -outdir \"DATA/label\" \\\n -val_size 0.2\n```\n\n### (3) Train\n```bash\nmousechd train_clf \\\n -exp_dir \"OUTPUTS/Classifier\" \\\n -exp [EXPERIEMENT_NAME] \\\n -data_dir \"DATA/resampled\" \\\n -label_dir \"DATA/label/x5_base/1fold\" \\\n -epochs [NUM_EPOCHS]\n```\n\n### (4) Evaluate retrained model\n```bash\nmousechd test_clf \\\n -model_dir \"OUTPUTS/Classifier/<EXPERIMENT_NAME>\" \\\n -imdir \"DATA/processed/images\" \\\n -maskdir \"OUTPUTS/HeartSeg\" \\\n -stage [\"eval\"|\"test\"] \\\n -label [PATH/TO/CSV/TEST/FILE] \\ # <optional> if stage is \"eval\", -label must be specified\n -outdir [PATH/TO/OUTPUT/DIRECTORY]\n```\n\n</details>\n\n## GradCAM\n```bash\nmousechd explain \\\n-exp_dir \"OUTPUTS/Classifier/<EXPERIMENT_NAME>\" \\\n-imdir \"DATA/resampled/images\" \\\n-outdir [PATH/TO/OUTPUT/DIRECTORY]\n```\n\n## Analysis\nA detailed analysis can be found in the folder [analysis](./analysis/).\n\nFor some visualization, [Napari](https://napari.org/stable/) is required. To install: `pip install \"napari[all]`.\n\n\n## Acknowledgements\n* INCEPTION funding: [INCEPTION](https://www.inception-program.fr/en) \n* GPU server technical support: Quang Tru Huynh\n",
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