  Clinically deployed segmentation models are known to fail on data outside of
their training distribution. As these models perform well on most cases, it is
imperative to detect out-of-distribution (OOD) images at inference to protect
against automation bias. This work applies the Mahalanobis distance post hoc to
the bottleneck features of a Swin UNETR model that segments the liver on
T1-weighted magnetic resonance imaging. By reducing the dimensions of the
bottleneck features with principal component analysis, OOD images were detected
with high performance and minimal computational load.
