Due to the constraints of the imaging device and high cost in operation time,
computer tomography (CT) scans are usually acquired with low intra-slice
resolution. Improving the intra-slice resolution is beneficial to the disease
diagnosis for both human experts and computer-aided systems. To this end, this
paper builds a novel medical slice synthesis to increase the between-slice
resolution. Considering that the ground-truth intermediate medical slices are
always absent in clinical practice, we introduce the incremental cross-view
mutual distillation strategy to accomplish this task in the self-supervised
learning manner. Specifically, we model this problem from three different
views: slice-wise interpolation from axial view and pixel-wise interpolation
from coronal and sagittal views. Under this circumstance, the models learned
from different views can distill valuable knowledge to guide the learning
processes of each other. We can repeat this process to make the models
synthesize intermediate slice data with increasing inter-slice resolution. To
demonstrate the effectiveness of the proposed approach, we conduct
comprehensive experiments on a large-scale CT dataset. Quantitative and
qualitative comparison results show that our method outperforms
state-of-the-art algorithms by clear margins.

本文提出了一种基于自监督学习的医疗切片合成方法，通过不同视角下的学习和交互相互指导，从而实现在不需要中间切片的情况下增加切片间分辨率，经过大量量化和定性实验验证的结果表明，所提方法明显优于同类算法。