  In medical imaging, scans often reveal objects with varied contrasts but
consistent internal intensities or textures. This characteristic enables the
use of low-frequency approximations for tasks such as segmentation and
deformation field estimation. Yet, integrating this concept into neural network
architectures for medical image analysis remains underexplored. In this paper,
we propose the Slicer Network, a novel architecture designed to leverage these
traits. Comprising an encoder utilizing models like vision transformers for
feature extraction and a slicer employing a learnable bilateral grid, the
Slicer Network strategically refines and upsamples feature maps via a
splatting-blurring-slicing process. This introduces an edge-preserving
low-frequency approximation for the network outcome, effectively enlarging the
effective receptive field. The enhancement not only reduces computational
complexity but also boosts overall performance. Experiments across different
medical imaging applications, including unsupervised and keypoints-based image
registration and lesion segmentation, have verified the Slicer Network's
improved accuracy and efficiency.
