  Leading neuroimaging studies have pushed 3T MRI acquisition resolutions below
1.0 mm for improved structure definition and morphometry. Yet, only few,
time-intensive automated image analysis pipelines have been validated for
high-resolution (HiRes) settings. Efficient deep learning approaches, on the
other hand, rarely support more than one fixed resolution (usually 1.0 mm).
Furthermore, the lack of a standard submillimeter resolution as well as limited
availability of diverse HiRes data with sufficient coverage of scanner, age,
diseases, or genetic variance poses additional, unsolved challenges for
training HiRes networks. Incorporating resolution-independence into deep
learning-based segmentation, i.e., the ability to segment images at their
native resolution across a range of different voxel sizes, promises to overcome
these challenges, yet no such approach currently exists. We now fill this gap
by introducing a Voxelsize Independent Neural Network (VINN) for
resolution-independent segmentation tasks and present FastSurferVINN, which (i)
establishes and implements resolution-independence for deep learning as the
first method simultaneously supporting 0.7-1.0 mm whole brain segmentation,
(ii) significantly outperforms state-of-the-art methods across resolutions, and
(iii) mitigates the data imbalance problem present in HiRes datasets. Overall,
internal resolution-independence mutually benefits both HiRes and 1.0 mm MRI
segmentation. With our rigorously validated FastSurferVINN we distribute a
rapid tool for morphometric neuroimage analysis. The VINN architecture,
furthermore, represents an efficient resolution-independent segmentation method
for wider application
