title: A framework for the objective assessment of registration accuracy.
abstract: Validation and accuracy assessment are the main 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: intrasubject rigid and affine registration of magnetic resonance images. 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 proposed model 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.
authors.affiliation: Department of Psychology, Royal Holloway, University of London, Egham TW20 0EX, UK ; Department of Computer Science, University of Verona, 37134 Verona, Italy.
authors.forename: Francesca
authors.identifier: 0000-0003-2073-0783
authors.initials: F
authors.lastname: Pizzorni Ferrarese
authors.affiliation: Department of Computer Science, University of Verona, 37134 Verona, Italy.
authors.forename: Flavio
authors.initials: F
authors.lastname: Simonetti
authors.affiliation: Department of Neurological, Neuropsychological, Morphological and Movement Sciences, University of Verona, 37126 Verona, Italy.
authors.forename: Roberto Israel
authors.initials: RI
authors.lastname: Foroni
authors.affiliation: Department of Computer Science, University of Verona, 37134 Verona, Italy.
authors.forename: Gloria
authors.identifier: 0000-0002-6889-3461
authors.initials: G
authors.lastname: Menegaz