Weighted Metamorphosis for Registration of Images with Different Topologies

Anton François; Matthis Maillard; Catherine Oppenheim; Johan Pallud; Isabelle Bloch; Pietro Gori; Joan Glaunès

doi:10.1007/978-3-031-11203-4_2

Weighted Metamorphosis for Registration of Images with Different Topologies

Anton François
, Matthis Maillard
, Catherine Oppenheim
, Johan Pallud
, Isabelle Bloch
, Pietro Gori
, Joan Glaunès

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

We present an extension of the Metamorphosis algorithm to align images with different topologies and/or appearances. We propose to restrict/limit the metamorphic intensity additions using a time-varying spatial weight function. It can be used to model prior knowledge about the topological/appearance changes (e.g., tumour/oedema). We show that our method improves the disentanglement between anatomical (i.e., shape) and topological (i.e., appearance) changes, thus improving the registration interpretability and its clinical usefulness. As clinical application, we validated our method using MR brain tumour images from the BraTS 2021 dataset. We showed that our method can better align healthy brain templates to images with brain tumours than existing state-of-the-art methods. Our PyTorch code is freely available here: https://github.com/antonfrancois/Demeter_metamorphosis.

langue originale	Anglais
titre	Biomedical Image Registration - 10th International Workshop, WBIR 2022, Proceedings
rédacteurs en chef	Alessa Hering, Julia Schnabel, Miaomiao Zhang, Enzo Ferrante, Mattias Heinrich, Daniel Rueckert
Editeur	Springer Science and Business Media Deutschland GmbH
Pages	8-17
Nombre de pages	10
ISBN (imprimé)	9783031112027
Les DOIs	https://doi.org/10.1007/978-3-031-11203-4_2
état	Publié - 1 janv. 2022
Evénement	10th International Workshop on Biomedical Image Registration, WBIR 2020 - Munich, Allemagne Durée: 10 juil. 2022 → 12 juil. 2022

Série de publications

Nom	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13386 LNCS
ISSN (imprimé)	0302-9743
ISSN (Electronique)	1611-3349

Une conférence

Une conférence	10th International Workshop on Biomedical Image Registration, WBIR 2020
Pays/Territoire	Allemagne
La ville	Munich
période	10/07/22 → 12/07/22

Accès au document

10.1007/978-3-031-11203-4_2

Autres fichiers et liens

Lien vers la publication dans Scopus

Contient cette citation

François, A., Maillard, M., Oppenheim, C., Pallud, J., Bloch, I., Gori, P., & Glaunès, J. (2022). Weighted Metamorphosis for Registration of Images with Different Topologies. Dans A. Hering, J. Schnabel, M. Zhang, E. Ferrante, M. Heinrich, & D. Rueckert (eds.), Biomedical Image Registration - 10th International Workshop, WBIR 2022, Proceedings (p. 8-17). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol 13386 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-11203-4_2

François, Anton ; Maillard, Matthis ; Oppenheim, Catherine et al. / Weighted Metamorphosis for Registration of Images with Different Topologies. Biomedical Image Registration - 10th International Workshop, WBIR 2022, Proceedings. Editeur / Alessa Hering ; Julia Schnabel ; Miaomiao Zhang ; Enzo Ferrante ; Mattias Heinrich ; Daniel Rueckert. Springer Science and Business Media Deutschland GmbH, 2022. p. 8-17 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Weighted Metamorphosis for Registration of Images with Different Topologies",

abstract = "We present an extension of the Metamorphosis algorithm to align images with different topologies and/or appearances. We propose to restrict/limit the metamorphic intensity additions using a time-varying spatial weight function. It can be used to model prior knowledge about the topological/appearance changes (e.g., tumour/oedema). We show that our method improves the disentanglement between anatomical (i.e., shape) and topological (i.e., appearance) changes, thus improving the registration interpretability and its clinical usefulness. As clinical application, we validated our method using MR brain tumour images from the BraTS 2021 dataset. We showed that our method can better align healthy brain templates to images with brain tumours than existing state-of-the-art methods. Our PyTorch code is freely available here: https://github.com/antonfrancois/Demeter\_metamorphosis.",

keywords = "Brain tumour, Image registration, Metamorphosis, Topology variation",

author = "Anton Fran{\c c}ois and Matthis Maillard and Catherine Oppenheim and Johan Pallud and Isabelle Bloch and Pietro Gori and Joan Glaun{\`e}s",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 10th International Workshop on Biomedical Image Registration, WBIR 2020 ; Conference date: 10-07-2022 Through 12-07-2022",

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doi = "10.1007/978-3-031-11203-4\_2",

language = "English",

isbn = "9783031112027",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "8--17",

editor = "Alessa Hering and Julia Schnabel and Miaomiao Zhang and Enzo Ferrante and Mattias Heinrich and Daniel Rueckert",

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François, A, Maillard, M, Oppenheim, C, Pallud, J, Bloch, I, Gori, P & Glaunès, J 2022, Weighted Metamorphosis for Registration of Images with Different Topologies. Dans A Hering, J Schnabel, M Zhang, E Ferrante, M Heinrich & D Rueckert (eds), Biomedical Image Registration - 10th International Workshop, WBIR 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), VOL. 13386 LNCS, Springer Science and Business Media Deutschland GmbH, p. 8-17, 10th International Workshop on Biomedical Image Registration, WBIR 2020, Munich, Allemagne, 10/07/22. https://doi.org/10.1007/978-3-031-11203-4_2

Weighted Metamorphosis for Registration of Images with Different Topologies. / François, Anton; Maillard, Matthis; Oppenheim, Catherine et al.
Biomedical Image Registration - 10th International Workshop, WBIR 2022, Proceedings. Ed. / Alessa Hering; Julia Schnabel; Miaomiao Zhang; Enzo Ferrante; Mattias Heinrich; Daniel Rueckert. Springer Science and Business Media Deutschland GmbH, 2022. p. 8-17 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol 13386 LNCS).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

TY - GEN

T1 - Weighted Metamorphosis for Registration of Images with Different Topologies

AU - François, Anton

AU - Maillard, Matthis

AU - Oppenheim, Catherine

AU - Pallud, Johan

AU - Bloch, Isabelle

AU - Gori, Pietro

AU - Glaunès, Joan

PY - 2022/1/1

Y1 - 2022/1/1

N2 - We present an extension of the Metamorphosis algorithm to align images with different topologies and/or appearances. We propose to restrict/limit the metamorphic intensity additions using a time-varying spatial weight function. It can be used to model prior knowledge about the topological/appearance changes (e.g., tumour/oedema). We show that our method improves the disentanglement between anatomical (i.e., shape) and topological (i.e., appearance) changes, thus improving the registration interpretability and its clinical usefulness. As clinical application, we validated our method using MR brain tumour images from the BraTS 2021 dataset. We showed that our method can better align healthy brain templates to images with brain tumours than existing state-of-the-art methods. Our PyTorch code is freely available here: https://github.com/antonfrancois/Demeter_metamorphosis.

AB - We present an extension of the Metamorphosis algorithm to align images with different topologies and/or appearances. We propose to restrict/limit the metamorphic intensity additions using a time-varying spatial weight function. It can be used to model prior knowledge about the topological/appearance changes (e.g., tumour/oedema). We show that our method improves the disentanglement between anatomical (i.e., shape) and topological (i.e., appearance) changes, thus improving the registration interpretability and its clinical usefulness. As clinical application, we validated our method using MR brain tumour images from the BraTS 2021 dataset. We showed that our method can better align healthy brain templates to images with brain tumours than existing state-of-the-art methods. Our PyTorch code is freely available here: https://github.com/antonfrancois/Demeter_metamorphosis.

KW - Brain tumour

KW - Image registration

KW - Metamorphosis

KW - Topology variation

UR - https://www.scopus.com/pages/publications/85135014307

U2 - 10.1007/978-3-031-11203-4_2

DO - 10.1007/978-3-031-11203-4_2

M3 - Conference contribution

AN - SCOPUS:85135014307

SN - 9783031112027

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 8

EP - 17

BT - Biomedical Image Registration - 10th International Workshop, WBIR 2022, Proceedings

A2 - Hering, Alessa

A2 - Schnabel, Julia

A2 - Zhang, Miaomiao

A2 - Ferrante, Enzo

A2 - Heinrich, Mattias

A2 - Rueckert, Daniel

PB - Springer Science and Business Media Deutschland GmbH

T2 - 10th International Workshop on Biomedical Image Registration, WBIR 2020

Y2 - 10 July 2022 through 12 July 2022

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François A, Maillard M, Oppenheim C, Pallud J, Bloch I, Gori P et al. Weighted Metamorphosis for Registration of Images with Different Topologies. Dans Hering A, Schnabel J, Zhang M, Ferrante E, Heinrich M, Rueckert D, rédacteurs en chef, Biomedical Image Registration - 10th International Workshop, WBIR 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 8-17. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-11203-4_2