mlVIRNET: Multilevel Variational Image Registration Network

Alessa Hering*, Bram van Ginneken, Stefan Heldmann

*Corresponding author for this work


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.

Original languageEnglish
Title of host publicationMICCAI 2019: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
EditorsDinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan
Number of pages9
Volume11769 LNCS
PublisherSpringer, Cham
Publication date10.10.2019
ISBN (Print)978-3-030-32225-0
ISBN (Electronic)978-3-030-32226-7
Publication statusPublished - 10.10.2019
Event22nd International Conference on Medical Image Computing and Computer-Assisted Intervention - Shenzhen, China
Duration: 13.10.201917.10.2019
Conference number: 232939


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