py-SAINT 1.7.0

This is SAINT(Spatially Aware Interpolation Network for Medical Slice Synthesis)

1. .nii->.pt

   from py_SAINT.STAGE1 import nii2pickle
   
   nii2pickle.nii2pt(ori_dir_path,output_file_path)
   

   parameters	description
   ori_dir_path	file path containing .nii
   output_file_path	file path of the generated .pt

   #eg:
   
   nii2pickle.nii2pt("/home1/xx/xx_data/273data-yscl/1T2/1/002_OCor_T2_FRFSE/","/home1/xx/SAINT/Data/Stage1_Input/TEST/HR/")
   

2. Interpolate with sag and cor view respectively

   from py_SAINT.STAGE1 import interpolation
   
   interpolation.get_Stage1_result (scale ='4',save =/path/ ,dir_data ='/path/',n_colors =3 ,n_GPUs =1,rgb_range =4000, view ='sag',gpu='0')
   
   interpolation.get_Stage1_result (scale ='4',save =/path/ ,dir_data ='/path/',n_colors =3 ,n_GPUs =1,rgb_range =4000, view ='cor',gpu='0')
   

   parameters	description
   scale	super resolution scale (eg:2,3,4,6)
   save	file path of save
   dir_data	dataset directory (Note that the dir_data path should point to a folder that contains subfolders named 'TEST', each of which needs to have a 'HR' and 'LR' subfolder, 'HR' is high resolution file , 'LR' is low resolution file. Data should go accordingly in this structure.
   n_colors	number of channels to use
   n_GPUs	number of GPUs
   rgb_range	maximum value of RGB
   view	view of interpolation (Note the --view option performs inference on the volume from either the sagittal or coronal axis. Note that the whether it's actually sagittal or coronal depends on the orientation of the data.)

   #eg:
   
   interpolation.get_Stage1_result (scale ='4',save ="/home1/xx/SAINT/Data/Stage1_output_sag_cor/" ,dir_data ='/home1/xx/SAINT/Data/Stage1_Input/',n_colors =3 ,n_GPUs =1,rgb_range =4000, view ='cor',gpu='0')
   
   interpolation.get_Stage1_result (scale ='4',save ="/home1/xx/SAINT/Data/Stage1_output_sag_cor/" ,dir_data ='/home1/xx/SAINT/Data/Stage1_Input/',n_colors =3 ,n_GPUs =1,rgb_range =4000, view ='sag',gpu='0')
   

3. Before going to the RFN stage, sagittal and coronal-wise SR'ed volume needs to be recombine into a single volume for inference. In simple terms just concatenate them in the first dimension, coronal SR goes in channel 0 and sagittal SR goes in channel 1

   from py_SAINT.STAGE1.process import cor_sag_comb_test
   
   cor_sag_comb_test.comb_cor_sag(files_dir='/path/',input_sag_cor_dir='/path/',out_dir='/path/', scale=4)
   

   parameters	description
   files_dir	dataset directory
   input_sag_cor_dir	path to the folder containing sag and cor
   out_dir	generated combine path
   scale	super resolution scale

   #eg:
   
   cor_sag_comb_test.comb_cor_sag(files_dir='/home1/mksun/SAINT/Data/Stage1_Input/TEST/HR/',input_sag_cor_dir='/home1/mksun/SAINT/Data/Stage1_output_sag_cor/results/raw/',out_dir='/home1/mksun/SAINT/Data/combine_cor_sag_out/TEST/', scale=4)
   

4. Residual-Fusion

   from py_SAINT.STAGE2 import fuse
   
   fuse.get_Stage2_result(save ='/path/',dir_data ='/path/' ,n_GPUs =1 ,rgb_range =4000,gpu='0')
   

   parameters	description
   save	file path of save
   dir_data	step3_out_dir
   n_GPUs	number of GPUs
   rgb_range	maximum value of RGB

   #eg:
   
   fuse.get_Stage2_result(save ='/home1/mksun/SAINT/Data/out_fuse/',dir_data ='/home1/mksun/SAINT/Data/combine_cor_sag_out/' ,n_GPUs =1 ,rgb_range =4000,gpu='0')
   

5. .pt->.nii(option)

   from py_SAINT.STAGE1 import pt2nii
   
   pt2nii.pt2nii(ori_nii_dir_path, pt_dir_path,nii_dir_path)
   

   parameters	description
   nii_dir_path	nii_output_dir

   #eg:
   
   pt2nii.pt2nii(ori_nii_dir_path='/home1/mksun/xh_data/273data-yscl/1T2/1/002_OCor_T2_FRFSE/',pt_dir_path='/home1/mksun/SAINT/Data/out_fuse/results/raw/',nii_dir_path='/home1/mksun/SAINT/Data/final_nii/')