Validation and accuracy assessment are themain bottlenecks preventing the adoption of image processing algorithms in the clinical
practice. In the classical approach, a posteriori analysis is performed through objective metrics. In this work, a different approach
based on Petri nets is proposed.The basic idea consists in predicting the accuracy of a given pipeline based on the identification
and characterization of the sources of inaccuracy. The concept is demonstrated on a case study: the intrasubject rigid and affine
registration of magnetic resonance images. A choice of possible sources of inaccuracies that can affect the registration process is
accounted for, and an estimation of the overall inaccuracy is provided through Petri nets. Both synthetic and real data are considered.
While synthetic data allow the benchmarking of the performance with respect to the ground truth, real data enable to assess the
robustness of the methodology in real contexts as well as to determine the suitability of the use of synthetic data in the training
phase. Results revealed a higher correlation and a lower dispersion among the metrics for simulated data, while the opposite trend
was observed for pathologic ones. Results show that the proposedmodel not only provides a good prediction performance but also
leads to the optimization of the end-to-end chain in terms of accuracy and robustness, setting the ground for its generalization to
different and more complex scenarios