mlVIRNET: Multilevel Variational Image Registration Network

Alessa Hering; Bram van Ginneken; Stefan Heldmann

doi:10.1007/978-3-030-32226-7_29

mlVIRNET: Multilevel Variational Image Registration Network

Alessa Hering^*, Bram van Ginneken, Stefan Heldmann

^*Corresponding author for this work

Institute of Mathematics and Image Computing

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.

Original language	English
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
Volume	11769 LNCS
Publisher	Springer, Cham
Publication date	10.10.2019
Pages	257-265
ISBN (Print)	978-3-030-32225-0
ISBN (Electronic)	978-3-030-32226-7
DOIs	https://doi.org/10.1007/978-3-030-32226-7_29
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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-32226-7_29

Cite this

Hering, A., van Ginneken, B., & Heldmann, S. (2019). mlVIRNET: Multilevel Variational Image Registration Network. In D. Shen, T. Liu, T. M. Peters, L. H. Staib, C. Essert, S. Zhou, P.-T. Yap, & A. Khan (Eds.), MICCAI 2019: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 (Vol. 11769 LNCS, pp. 257-265). (Lecture Notes in Computer Science; Vol. 11769 LNCS). Springer, Cham. https://doi.org/10.1007/978-3-030-32226-7_29

Hering, Alessa ; van Ginneken, Bram ; Heldmann, Stefan. / mlVIRNET: Multilevel Variational Image Registration Network. MICCAI 2019: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. editor / Dinggang Shen ; Tianming Liu ; Terry M. Peters ; Lawrence H. Staib ; Caroline Essert ; Sean Zhou ; Pew-Thian Yap ; Ali Khan. Vol. 11769 LNCS Springer, Cham, 2019. pp. 257-265 (Lecture Notes in Computer Science).

@inproceedings{04a2363ec4a0411cbfe34fb1bb46e17e,

title = "mlVIRNET: Multilevel Variational Image Registration Network",

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.",

author = "Alessa Hering and {van Ginneken}, Bram and Stefan Heldmann",

note = "Funding Information: Acknowledgements. We gratefully acknowledge the COPDGene Study for providing the data used. COPDGene is funded by Award Number U01 HL089897 and Award Number U01 HL089856 from the National Heart, Lung, and Blood Institute. The COPDGene project is also supported by the COPD Foundation through contributions made to an Industry Advisory Board comprised of AstraZeneca, Boehringer Ingelheim, GlaxoSmithKline, Novartis, Pfizer, Siemens and Sunovion. Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention , MICCAI 2019 ; Conference date: 13-10-2019 Through 17-10-2019",

year = "2019",

month = oct,

day = "10",

doi = "10.1007/978-3-030-32226-7_29",

language = "English",

isbn = "978-3-030-32225-0",

volume = "11769 LNCS",

series = "Lecture Notes in Computer Science",

publisher = "Springer, Cham",

pages = "257--265",

editor = "Shen, {Dinggang } and Liu, {Tianming } and {M. Peters}, {Terry } and {H. Staib}, {Lawrence } and Essert, {Caroline } and Zhou, {Sean } and Yap, {Pew-Thian } and Khan, {Ali }",

booktitle = "MICCAI 2019: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019",

}

Hering, A, van Ginneken, B & Heldmann, S 2019, mlVIRNET: Multilevel Variational Image Registration Network. in D Shen, T Liu, T M. Peters, L H. Staib, C Essert, S Zhou, P-T Yap & A Khan (eds), MICCAI 2019: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. vol. 11769 LNCS, Lecture Notes in Computer Science, vol. 11769 LNCS, Springer, Cham, pp. 257-265, 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention , Shenzhen, China, 13.10.19. https://doi.org/10.1007/978-3-030-32226-7_29

mlVIRNET: Multilevel Variational Image Registration Network. / Hering, Alessa; van Ginneken, Bram; Heldmann, Stefan.
MICCAI 2019: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. ed. / Dinggang Shen; Tianming Liu; Terry M. Peters; Lawrence H. Staib; Caroline Essert; Sean Zhou; Pew-Thian Yap; Ali Khan. Vol. 11769 LNCS Springer, Cham, 2019. p. 257-265 (Lecture Notes in Computer Science; Vol. 11769 LNCS).

Research output: Chapters in Books/Reports/Conference Proceedings › Conference contribution › peer-review

TY - GEN

T1 - mlVIRNET: Multilevel Variational Image Registration Network

AU - Hering, Alessa

AU - van Ginneken, Bram

AU - Heldmann, Stefan

N1 - Conference code: 232939

PY - 2019/10/10

Y1 - 2019/10/10

N2 - 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.

AB - 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.

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85075819045&origin=inward&txGid=17afaf111c3052ed2487f38095d4849b

U2 - 10.1007/978-3-030-32226-7_29

DO - 10.1007/978-3-030-32226-7_29

M3 - Conference contribution

SN - 978-3-030-32225-0

VL - 11769 LNCS

T3 - Lecture Notes in Computer Science

SP - 257

EP - 265

BT - MICCAI 2019: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

A2 - Shen, Dinggang

A2 - Liu, Tianming

A2 - M. Peters, Terry

A2 - H. Staib, Lawrence

A2 - Essert, Caroline

A2 - Zhou, Sean

A2 - Yap, Pew-Thian

A2 - Khan, Ali

PB - Springer, Cham

T2 - 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention

Y2 - 13 October 2019 through 17 October 2019

ER -

Hering A, van Ginneken B, Heldmann S. mlVIRNET: Multilevel Variational Image Registration Network. In Shen D, Liu T, M. Peters T, H. Staib L, Essert C, Zhou S, Yap PT, Khan A, editors, MICCAI 2019: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. Vol. 11769 LNCS. Springer, Cham. 2019. p. 257-265. (Lecture Notes in Computer Science). doi: 10.1007/978-3-030-32226-7_29

mlVIRNET: Multilevel Variational Image Registration Network

Abstract

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this