  Medical images are often acquired in different settings, requiring
harmonization to adapt to the operating point of algorithms. Specifically, to
standardize the physical spacing of imaging voxels in heterogeneous inference
settings, images are typically resampled before being processed by deep
learning models. However, down-sampling results in loss of information, whereas
upsampling introduces redundant information leading to inefficient resource
utilization. To overcome these issues, we propose to condition segmentation
models on the voxel spacing using hypernetworks. Our approach allows processing
images at their native resolutions or at resolutions adjusted to the hardware
and time constraints at inference time. Our experiments across multiple
datasets demonstrate that our approach achieves competitive performance
compared to resolution-specific models, while offering greater flexibility for
the end user. This also simplifies model development, deployment and
maintenance. Our code is available at
https://github.com/ImFusionGmbH/HyperSpace.
