  Computed tomography (CT) segmentation models frequently include classes that
are not currently supported by magnetic resonance imaging (MRI) segmentation
models. In this study, we show that a simple image inversion technique can
significantly improve the segmentation quality of CT segmentation models on MRI
data, by using the TotalSegmentator model, applied to T1-weighted MRI images,
as example. Image inversion is straightforward to implement and does not
require dedicated graphics processing units (GPUs), thus providing a quick
alternative to complex deep modality-transfer models for generating
segmentation masks for MRI data.
