R2SNs provide a novel, reliable and biologically plausible method to understand human morphological covariance based on sMRI.

If you use R2SN_code, please cite this orignal paper: Original Paper

Zhao K, Zheng Q, Che T, et al. Regional radiomics similarity networks (R2SNs) in the human brain: Reproducibility, small-world properties and a biological basis [J]. Network Neuroscience, 2021, 1-15.

1. Clone this repo

2. Navigate to the main R2SN_construction directory , then run:

   pip install .
   

   pip install R2SN

• Python >= 3.8
• Nibabel >= 3.2.1
• Pyradiomics >= 3.0.1
• Scipy >= 1.6.2
• SimpleITK >= 2.1.1.2
• NumPy >= 1.21.5
• Pandas >= 1.2.3
• Sklearn

from R2SN import feature_extraction  

if __name__ == '__main__':
	Image_path = r'D:\Python_project\venv\Brain\R2SN\R2SN\data\Image' 	       	# input image path  
	Network_output_path = r'D:\Python_project\venv\Brain\R2SN\R2SN\data\Network' 	# output network path  
	n_jobs = 1								# number of process
	feature_extraction.R2SN_feature_extract(Image_path = Image_path,  
                                           			Network_output_path = Network_output_path,  
                                            			n_jobs = n_jobs)  

Here, the image_path is the dir of your original file. The Network_out_path is the dir of your choosed ouput dir. n_jobs is means the number of jobs runing simultaneously.

Author: Fan Yang ([email protected]),Kun Zhao ([email protected])

Any questions, pls do not hesitate to contact [email protected]

The registeration section is based on ANTs toolkit.