Multimodal deformable image registration using contrastive learning of equivariant image representations

Nordling, Love, 1995- (author): Uppsala universitet,Bildanalys och människa-datorinteraktion,Avdelningen Vi3,MIDA

Öfverstedt, Johan (author): Uppsala universitet,Bildanalys och människa-datorinteraktion,Avdelningen Vi3,MIDA

Lindblad, Joakim (author): Uppsala universitet,Bildanalys och människa-datorinteraktion,Avdelningen Vi3,MIDA

Sladoje, Nataša (author): Uppsala universitet,Bildanalys och människa-datorinteraktion,Avdelningen Vi3,MIDA

(creator_code:org_t)

Related links:: https://ssba.org.se/...; show more...; https://urn.kb.se/re...; show less...

Abstract Subject headings

We propose a method for multimodal deformable image registration which combines a powerful deep learning approach to generate CoMIRs, dense image-like representations of multimodal image pairs, with INSPIRE, a robust framework for monomodal deformable image registration. We introduce new equivariance constraints to improve the consistency of CoMIRs under deformation. We evaluate the method on three publicly available multimodal datasets, one remote sensing, one histological, and one cytological. The proposed method demonstrates general applicability and consistently outperforms state-of-the-art registration tools \elastixname and VoxelMorph. We share source code of the proposed method and complete experimental setup as open-source at: https://github.com/MIDA-group/CoMIR_INSPIRE.

By the author/editor: Nordling, Love, ...; Öfverstedt, Joha ...; Lindblad, Joakim; Sladoje, Nataša

About the subject

NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Computer Science ...

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.