title: Validation through accuracy prediction in neuroimage registration.
abstract: Validation and accuracy assessment are the main bottlenecks preventing the adoption of many medical image processing algorithms in the clinical practice. In the classical approach, a-posteriori analysis is performed based on some predefined objective metrics. The main limitation of this methodology is in the fact that it does not provide a mean to estimate what the performance would be a-priori, and thus to shape the processing workflow in the most suitable way. In this paper, we propose a different approach based on Petri Nets. The basic idea consists in predicting the accuracy that will result from a given processing on a given type of data based on the identification and characterization of the sources of inaccuracy intervening along the whole chain. Here we propose a proof of concept in the specific case of image registration. A Petri Net is constructed after the detection of the possible sources of inaccuracy and the evaluation of their respective impact on the estimation of the deformation field. A training set of five different synthetic volumes is used. Afterward, validation is performed on a different set of five synthetic volumes by comparing the estimated inaccuracy with the posterior measurements according to a set of predefined metrics. Two real cases are also considered. Results show that the proposed model provides a good prediction performance. An extended set of clinical data will allow the complete characterization of the system for the considered task.
authors.affiliation: Department of Computer Science, University of Verona, Italy. francesca.pizzorniferrarese@univr.it
authors.forename: Francesca
authors.initials: F
authors.lastname: Pizzorni Ferrarese
authors.forename: Flavio
authors.initials: F
authors.lastname: Simonetti
authors.forename: Roberto
authors.initials: R
authors.lastname: Foroni
authors.forename: Gloria
authors.initials: G
authors.lastname: Menegaz
mesh_terms: D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*