Slice Imputation: Intermediate Slice Interpolation for Anisotropic 3D Medical Image Segmentation

TL;DR

This paper presents a novel slice imputation method that enhances 3D medical image segmentation by improving smoothness and isotropy in all three spatial directions, outperforming existing methods on various datasets.

Contribution

The study introduces a multitask interpolation approach with a smoothness loss to improve anisotropic 3D medical image volumes in all directions, leading to better segmentation accuracy.

Findings

Outperforms existing slice imputation methods on CT and MRI datasets.

Improves isotropy and smoothness of interpolated 3D volumes.

Enhances segmentation accuracy in brain, liver, and prostate imaging.

Abstract

We introduce a novel frame-interpolation-based method for slice imputation to improve segmentation accuracy for anisotropic 3D medical images, in which the number of slices and their corresponding segmentation labels can be increased between two consecutive slices in anisotropic 3D medical volumes. Unlike previous inter-slice imputation methods, which only focus on the smoothness in the axial direction, this study aims to improve the smoothness of the interpolated 3D medical volumes in all three directions: axial, sagittal, and coronal. The proposed multitask inter-slice imputation method, in particular, incorporates a smoothness loss function to evaluate the smoothness of the interpolated 3D medical volumes in the through-plane direction (sagittal and coronal). It not only improves the resolution of the interpolated 3D medical volumes in the through-plane direction but also transforms them into isotropic representations, which leads to better segmentation performances. Experiments on whole tumor segmentation in the brain, liver tumor segmentation, and prostate segmentation indicate that our method outperforms the competing slice imputation methods on both computed tomography and magnetic resonance images volumes in most cases.