  Clinically deployed deep learning-based segmentation models are known to fail
on data outside of their training distributions. While clinicians review the
segmentations, these models tend to perform well in most instances, which could
exacerbate automation bias. Therefore, detecting out-of-distribution images at
inference is critical to warn the clinicians that the model likely failed. This
work applied the Mahalanobis distance (MD) post hoc to the bottleneck features
of four Swin UNETR and nnU-net models that segmented the liver on T1-weighted
magnetic resonance imaging and computed tomography. By reducing the dimensions
of the bottleneck features with either principal component analysis or uniform
manifold approximation and projection, images the models failed on were
detected with high performance and minimal computational load. In addition,
this work explored a non-parametric alternative to the MD, a k-th nearest
neighbors distance (KNN). KNN drastically improved scalability and performance
over MD when both were applied to raw and average-pooled bottleneck features.
