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Image interpolation in medical area is of high importance as most 3Dbiomedical volume images are sampled where the distance between consecutiveslices significantly greater than the in-plane pixel size due to radiation doseor scanning time. Image interpolation creates a number of new slices betweenknown slices in order to obtain an isotropic volume image. The results can beused for the higher quality of 3D reconstruction and visualization of humanbody structures. Semantic interpolation on the manifold has been proved to bevery useful for smoothing image interpolation. Nevertheless, all previousmethods focused on low-resolution image interpolation, and most of them workpoorly on high-resolution image. We propose a novel network, High ResolutionInterpolation Network (HRINet), aiming at producing high-resolution CT imageinterpolations. We combine the idea of ACAI and GANs, and propose a novel ideaof alternative supervision method by applying supervised and unsupervisedtraining alternatively to raise the accuracy of human organ structures in CTwhile keeping high quality. We compare an MSE based and a perceptual based lossoptimizing methods for high quality interpolation, and show the tradeoffbetween the structural correctness and sharpness. Our experiments show thegreat improvement on 256 2 and 5122 images quantitatively and qualitatively.

HRINet: Alternative Supervision Network for High-resolution CT image Interpolation