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)
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