A General Skull Stripping of Multiparametric Brain MRIs Using 3D Convolutional Neural Network

The objective of the project is to strip skull for multiparametric MRIs (mpMRIs). Users can find the pre-trained model in folder: pre-trained-model. The proposed method is convolutional neural network (CNN)-based. The algorithim is implemented in Python. It works for healthy brain mpMRIs, pre-operative mpMRIs, and post-operative mpMRIs. Furthermore, the algorithm is easy to custermize for any image modality (or any combinations). The proposed architecture is showing as:

There are skull stripping cases for healthy, pre-operative, and post-operative mpMRIs.

Required packages:

1. PyTorch (suggest version: 1.10.0)
2. configparser
3. SimpleITK
4. Numpy

5. Scipy

   Data structure

   
   last_train
   RRC_0001_fusion.nii.gz
   RRC_0002_fusion.nii.gz
   ...

last_test RRC_0003 RRC_0003_flair.nii.gz RRC_0003_t1.nii.gz RRC_0003_t1ce.nii.gz RRC_0003_t2.nii.gz RRC_0004 RRC_0004_flair.nii.gz RRC_0004_t1.nii.gz RRC_0004_t1ce.nii.gz RRC_0004_t2.nii.gz ...

## Usage
The configuration must be properly set up. Here are important configures:
* root_dir: parent directory for images
* model_path: directory to save the trained model
* num_ndf: initial number of channels
* s_modality: image modalities, such as f1c2 (where f: FLAIR, 1: T1, c:T1ce, 2: T2)
* num_class: 1 (for binary classification)
* num_epoch: number of epoches
* lr: initial learning rate
* transforms: data augmentation, such as random crop, etc.
* train_list: a text file contains the training sample file names
* valid_list: a text file contains the training sample file names
* img_dir: directory to training/validation data
### Train model (strongly recommend GPU)
To train your own model, the mpMRIs must be pre-processed following the pipeline. The mpMRIs needs to be normalized and stacked with sequence of FLAIR, T1, T1ce, and T2.
![pipleline](./supplement/pipeline.png)
Once the mpMRIs is preprocessed, execute the following code to train your own model.
>python train.py
### Inference 
Once you have a trained model (or use the pre-trained model), it is very easy to apply the model to your test images. Note that it requires all four image modalities to use the pre-trained model. You only set the right directory to the test data in config.ini file. The test_online.py automatically normalize the mpMRIs. To get the skull stripping, execute the following code:
>python test_online.py

### State-of-the-art comparison
We also compare the proposed method to popular methods/tools (including traditional methods and recent deep learning-based methods). Here is the comparison.
![comparison](supplement/comparison%20of%20state-of-the-art.png)

### Citation
Pei, L., Murat, A.K., Tahon, N.H.M. et al. A general skull stripping of multiparametric brain MRIs using 3D convolutional neural network. Sci Rep 12, 10826 (2022). 
[https://doi.org/10.1038/s41598-022-14983-4](https://doi.org/10.1038/s41598-022-14983-4)