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Project description

This is SAINT(Spatially Aware Interpolation Network for Medical Slice Synthesis)
  1. .nii->.pt

    from py_SAINT.STAGE1 import nii2pickle
    parameters description
    ori_dir_path file path containing .nii
    output_file_path file path of the generated .pt
  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.)
    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
    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
    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

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