Deep Slice Interpolation via Marginal Super-Resolution, Fusion and Refinement
This work addresses slice interpolation in brain MRI scans for medical imaging applications, representing an incremental improvement by fusing orthogonal information.
The authors tackled the problem of interpolating anisotropic brain MRI scans along the under-sampled axial direction by proposing a marginal super-resolution approach using 2D CNNs, which outperformed traditional linear interpolation and baseline 2D/3D CNN methods in experiments.
We propose a marginal super-resolution (MSR) approach based on 2D convolutional neural networks (CNNs) for interpolating an anisotropic brain magnetic resonance scan along the highly under-sampled direction, which is assumed to axial without loss of generality. Previous methods for slice interpolation only consider data from pairs of adjacent 2D slices. The possibility of fusing information from the direction orthogonal to the 2D slices remains unexplored. Our approach performs MSR in both sagittal and coronal directions, which provides an initial estimate for slice interpolation. The interpolated slices are then fused and refined in the axial direction for improved consistency. Since MSR consists of only 2D operations, it is more feasible in terms of GPU memory consumption and requires fewer training samples compared to 3D CNNs. Our experiments demonstrate that the proposed method outperforms traditional linear interpolation and baseline 2D/3D CNN-based approaches. We conclude by showcasing the method's practical utility in estimating brain volumes from under-sampled brain MR scans through semantic segmentation.