Abstract
We present a novel multilevel approach for deep learning based image registration. Recently published deep learning based registration methods have shown promising results for a wide range of tasks. However, these algorithms are still limited to relatively small deformations. Our method addresses this shortcoming by introducing a multilevel framework, which computes deformation fields on different scales, similar to conventional methods. Thereby, a coarse-level alignment is obtained first, which is subsequently improved on finer levels. We demonstrate our method on the complex task of inhale-to-exhale lung registration. We show that the use of a deep learning multilevel approach leads to significantly better registration results.
Originalsprache | Englisch |
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Titel | MICCAI 2019: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 |
Redakteure/-innen | Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan |
Seitenumfang | 9 |
Band | 11769 LNCS |
Herausgeber (Verlag) | Springer, Cham |
Erscheinungsdatum | 10.10.2019 |
Seiten | 257-265 |
ISBN (Print) | 978-3-030-32225-0 |
ISBN (elektronisch) | 978-3-030-32226-7 |
DOIs | |
Publikationsstatus | Veröffentlicht - 10.10.2019 |
Veranstaltung | 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention - Shenzhen, China Dauer: 13.10.2019 → 17.10.2019 Konferenznummer: 232939 |