  Image interpolation in medical area is of high importance as most 3D
biomedical volume images are sampled where the distance between consecutive
slices significantly greater than the in-plane pixel size due to radiation dose
or scanning time. Image interpolation creates a number of new slices between
known slices in order to obtain an isotropic volume image. The results can be
used for the higher quality of 3D reconstruction and visualization of human
body structures. Semantic interpolation on the manifold has been proved to be
very useful for smoothing image interpolation. Nevertheless, all previous
methods focused on low-resolution image interpolation, and most of them work
poorly on high-resolution image. We propose a novel network, High Resolution
Interpolation Network (HRINet), aiming at producing high-resolution CT image
interpolations. We combine the idea of ACAI and GANs, and propose a novel idea
of alternative supervision method by applying supervised and unsupervised
training alternatively to raise the accuracy of human organ structures in CT
while keeping high quality. We compare an MSE based and a perceptual based loss
optimizing methods for high quality interpolation, and show the tradeoff
between the structural correctness and sharpness. Our experiments show the
great improvement on 256 2 and 5122 images quantitatively and qualitatively.
