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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)

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	Open Access Gold (Paid Option)

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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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Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Correlative Microscopy ; Deep Learning ; Microscopy ; Unsupervised Multimodal Image Registration; Pathology

ISSN (print) / ISBN 1467-5463

e-ISSN 1477-4054

Journal Briefings in Bioinformatics

Quellenangaben Volume: 25, Issue: 2, Article Number: bbae029

Publisher Oxford University Press

Publishing Place Great Clarendon St, Oxford Ox2 6dp, England

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute of AI for Health (AIH)

Grants New National Excellence Program of the Ministry for Culture and Innovation
National Research, Development, and Innovation Fund