TY - GEN
T1 - Enhancing Label-Driven Deep Deformable Image Registration with Local Distance Metrics for State-of-the-Art Cardiac Motion Tracking
AU - Hering, Alessa
AU - Kuckertz, Sven
AU - Heldmann, Stefan
AU - Heinrich, Mattias P.
N1 - Publisher Copyright:
© 2019, Springer Berlin Heidelberg. All rights reserved.
PY - 2019
Y1 - 2019
N2 - While deep learning has achieved significant advances in accuracy for medical image segmentation, its benefits for deformable image registration have so far remained limited to reduced computation times. Previous work has either focused on replacing the iterative optimization of distance and smoothness terms with CNN-layers or using supervised approaches driven by labels. Our method is the first to combine the complementary strengths of global semantic information (represented by segmentation labels) and local distance metrics that help align surrounding structures. We demonstrate significant higher Dice scores (of 86.5%) for deformable cardiac image registration compared to classic registration (79.0%) as well as label-driven deep learning frameworks (83.4%).
AB - While deep learning has achieved significant advances in accuracy for medical image segmentation, its benefits for deformable image registration have so far remained limited to reduced computation times. Previous work has either focused on replacing the iterative optimization of distance and smoothness terms with CNN-layers or using supervised approaches driven by labels. Our method is the first to combine the complementary strengths of global semantic information (represented by segmentation labels) and local distance metrics that help align surrounding structures. We demonstrate significant higher Dice scores (of 86.5%) for deformable cardiac image registration compared to classic registration (79.0%) as well as label-driven deep learning frameworks (83.4%).
UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85065078034&origin=inward&txGid=c17ee17ddc1991a0346d845502081526
UR - http://www.mendeley.com/research/enhancing-labeldriven-deep-deformable-image-registration-local-distance-metrics-stateoftheart-cardia
U2 - 10.1007/978-3-658-25326-4_69
DO - 10.1007/978-3-658-25326-4_69
M3 - Conference contribution
SN - 978-3-658-25325-7
T3 - Informatik aktuell
SP - 309
EP - 314
BT - Bildverarbeitung für die Medizin 2019
PB - Springer Verlag
T2 - Bildverarbeitung für die Medizin 2019
Y2 - 17 March 2019 through 19 March 2019
ER -