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Titel:

Embedding Gradient-Based Optimization in Image Registration Networks

Dokumenttyp:
Proceedings Paper
Autor(en):
Qiu, Huaqi; Hammernik, Kerstin; Qin, Chen; Chen, Chen; Rueckert, Daniel
Abstract:
Deep learning (DL) image registration methods amortize the costly pair-wise iterative optimization by training deep neural networks to predict the optimal transformation in one fast forward-pass. In this work, we bridge the gap between traditional iterative energy optimization-based registration and network-based registration, and propose Gradient Descent Network for Image Registration (GraDIRN). Our proposed approach trains a DL network that embeds unrolled multi-resolution gradient-based energ...     »
Zeitschriftentitel:
Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv
Jahr:
2022
Band / Volume:
13436
Seitenangaben Beitrag:
56-65
Volltext / DOI:
doi:10.1007/978-3-031-16446-0_6
Print-ISSN:
0302-9743
TUM Einrichtung:
Institut für KI und Informatik in der Medizin (Prof. Rückert)
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