Improving anisotropy resolution of computed tomography and annotation using 3D super-resolution network.

In clinical practice, abdomen computed tomography (CT) is obtained with a slice thickness of 5 mm which causes the anisotropy resolution. Especially in 3D automatic medical segmentation tasks, this anisotropy resolution in transverse plane , z-axial of CT image causes the unbalance of spatial feature for 3D convolution, which further limits the quality of segmentation. To recover context features between slices, super-resolution networks can better reconstruct detail information than interpolation methods. To reconstruct CT from different scanner models, a stronger generalization ability is indispensable. Moreover, to improve segmentation performance, the annotation of lesion area should be reconstructed at the same time. To address these issues, an Average Super-Resolution Generative Adversarial Network (ASRGAN) is proposed in this paper. We designed a multi-path average block to recover inter-slice information from CT with different image quality. Experimental results demonstrate that the proposed ASRGAN is superior to other methods on reconstruction with 2.42 db improvement on PSNR. And based on its reconstruction results, it further promotes improving 3D segmentation of the abdominal lesion liver tumor by 4.00% and the abdominal viscera pancreas by 2.25% on dice, to further reveal the effects of our reconstruction from view of this follow-up medical image analysis.