  This paper investigates the numerical uncertainty of Convolutional Neural
Networks (CNNs) inference for structural brain MRI analysis. It applies Random
Rounding -- a stochastic arithmetic technique -- to CNN models employed in
non-linear registration (SynthMorph) and whole-brain segmentation (FastSurfer),
and compares the resulting numerical uncertainty to the one measured in a
reference image-processing pipeline (FreeSurfer recon-all). Results obtained on
32 representative subjects show that CNN predictions are substantially more
accurate numerically than traditional image-processing results (non-linear
registration: 19 vs 13 significant bits on average; whole-brain segmentation:
0.99 vs 0.92 S{\o}rensen-Dice score on average), which suggests a better
reproducibility of CNN results across execution environments.
