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.
|Title of host publication||MICCAI 2019: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019|
|Editors||Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan|
|Number of pages||9|
|Publication status||Published - 10.10.2019|
|Event||22nd International Conference on Medical Image Computing and Computer-Assisted Intervention - Shenzhen, China|
Duration: 13.10.2019 → 17.10.2019
Conference number: 232939