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Grexa, I.* ; Iván, Z.Z.* ; Migh, E.* ; Kovács, F.* ; Bolck, H.A.* ; Zheng, X.* ; Mund, A.* ; Moshkov, N.* ; Miczán, V.* ; Koos, K.* ; Horvath, P.

SuperCUT, an unsupervised multimodal image registration with deep learning for biomedical microscopy.

Brief. Bioinform. 25:bbae029 (2024)
Verlagsversion DOI PMC
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
Numerous imaging techniques are available for observing and interrogating biological samples, and several of them can be used consecutively to enable correlative analysis of different image modalities with varying resolutions and the inclusion of structural or molecular information. Achieving accurate registration of multimodal images is essential for the correlative analysis process, but it remains a challenging computer vision task with no widely accepted solution. Moreover, supervised registration methods require annotated data produced by experts, which is limited. To address this challenge, we propose a general unsupervised pipeline for multimodal image registration using deep learning. We provide a comprehensive evaluation of the proposed pipeline versus the current state-of-the-art image registration and style transfer methods on four types of biological problems utilizing different microscopy modalities. We found that style transfer of modality domains paired with fully unsupervised training leads to comparable image registration accuracy to supervised methods and, most importantly, does not require human intervention.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Correlative Microscopy ; Deep Learning ; Microscopy ; Unsupervised Multimodal Image Registration; Pathology
ISSN (print) / ISBN 1467-5463
e-ISSN 1477-4054
Zeitschrift Briefings in Bioinformatics
Quellenangaben Band: 25, Heft: 2, Seiten: , Artikelnummer: bbae029 Supplement: ,
Verlag Oxford University Press
Verlagsort Great Clarendon St, Oxford Ox2 6dp, England
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of AI for Health (AIH)
Förderungen New National Excellence Program of the Ministry for Culture and Innovation
National Research, Development, and Innovation Fund