This package includes inference codes supporting Super-resolution image and mask interpolations.
Project description
Designed for medical imaging data preprocesing, two types of normalization are implemented:
- Medical imaging mask inerpolation.
- SR image interpolation through Z directions (i.e., thick-slices to thin-slices) with arbitrary user-selected sampling ratios.
from KevinSR import mask_interpolation, SOUP_GAN
for mask interp
new_masks = mask_interpolation(masks, factor)
for SR image interp
thin_slices = SOUP_GAN(thick_slices, factor, prep_type)
#prep_type = 0 or 1 for different preprocessing types (thick-to-thin or thin-to-thin).
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