Segmenting hearts and screening congenital heart diseases in mice
Project description
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
This For user-friendly interface, refer to MouseCHD Napari plugin in this repository: https://github.com/hnguyentt/mousechd-napari
Raw data structure
It is recommended that your data is 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
......
Preprocessing
This step standardizes the data into the same spacing and view. Note: although this script supports different file formats, we recommend using NIFFTI format as it contains information about the spacing and orientation of the image.
mousechd preprocess.py \
-database <PATH/TO/DATABASE> \
-maskdir <PATH/TO/MASK/DIR> \ # relative to database
-masktype NIFFTI \ # relative to database
-metafile <PATH/TO/META/FILE> \
-outdir <PATH/TO/OUTPUT/DIR>
Heart segmentation
-
Preparing data for training and testing nnUNet:
mousechd segment -indir [INPUT/DIRECTORY] -outdir [OUTPUT/DIRECTORY]
-
Post-process nnUNet
mousechd postprocess_nnUNet \
-indir <PATH/TO/SEGMENTATION/BY/nnUNet> \
-outdir <PATH/TO/OUTDIR>
CHD detection
Analysis
For some visualizations (), [Napari] is required. To install: conda install -c conda-forge napari
A detailed analysis of the results can be found in the folder analysis.
Acknowledgements
- INCEPTION funding: INCEPTION
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