Anzeige
Erschienen in:
2018 | OriginalPaper | Buchkapitel
GDL-FIRE\(^\text {4D}\): Deep Learning-Based Fast 4D CT Image Registration
verfasst von : Thilo Sentker, Frederic Madesta, René Werner
Erschienen in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
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
Abstract
Deformable image registration (DIR) in thoracic 4D CT image data is integral for, e.g., radiotherapy treatment planning, but time consuming. Deep learning (DL)-based DIR promises speed-up, but present solutions are limited to small image sizes. In this paper, we propose a General Deep Learning-based Fast Image Registration framework suitable for application to clinical 4D CT data (GDL-FIRE\(^\text {4D}\)). Open source DIR frameworks are selected to build GDL-FIRE\(^\text {4D}\) variants. In-house-acquired 4D CT images serve as training and open 4D CT data repositories as external evaluation cohorts. Taking up current attempts to DIR uncertainty estimation, dropout-based uncertainty maps for GDL-FIRE\(^\text {4D}\) variants are analyzed. We show that (1) registration accuracy of GDL-FIRE\(^\text {4D}\) and standard DIR are in the same order; (2) computation time is reduced to a few seconds (here: 60-fold speed-up); and (3) dropout-based uncertainty maps do not correlate to across-DIR vector field differences, raising doubts about applicability in the given context.