  Deep Learning (DL) models tend to perform poorly when the data comes from a
distribution different from the training one. In critical applications such as
medical imaging, out-of-distribution (OOD) detection helps to identify such
data samples, increasing the model's reliability. Recent works have developed
DL-based OOD detection that achieves promising results on 2D medical images.
However, scaling most of these approaches on 3D images is computationally
intractable. Furthermore, the current 3D solutions struggle to achieve
acceptable results in detecting even synthetic OOD samples. Such limited
performance might indicate that DL often inefficiently embeds large volumetric
images. We argue that using the intensity histogram of the original CT or MRI
scan as embedding is descriptive enough to run OOD detection. Therefore, we
propose a histogram-based method that requires no DL and achieves almost
perfect results in this domain. Our proposal is supported two-fold. We evaluate
the performance on the publicly available datasets, where our method scores 1.0
AUROC in most setups. And we score second in the Medical Out-of-Distribution
challenge without fine-tuning and exploiting task-specific knowledge. Carefully
discussing the limitations, we conclude that our method solves the sample-level
OOD detection on 3D medical images in the current setting.
