Anzeige
Erschienen in:
2015 | OriginalPaper | Buchkapitel
Which Metrics Should Be Used in Non-linear Registration Evaluation?
verfasst von : Andre Santos Ribeiro, David J. Nutt, John McGonigle
Erschienen in: Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Non-linear registration is an essential step in neuroimaging, influencing both structural and functional analyses. Although important, how different registration methods influence the results of these analyses is poorly known, with the metrics used to compare methods weakly justified. In this work we propose a framework to simulate true deformation fields derived from manually segmented volumes of interest. We test both state-of-the-art binary and non-binary, volumetric and surface -based metrics against these true deformation fields. Our results show that surface-based metrics are twice as sensitive as volume-based metrics, but are typically less used in non-linear registration evaluations. All analysed metrics poorly explained the true deformation field, with none explaining more than half the variance.