Deformation equivariant cross-modality image synthesis with paired non-aligned training data
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2023-12
Department
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Mcode
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Language
en
Pages
13
1-13
1-13
Series
Medical Image Analysis, Volume 90
Abstract
Cross-modality image synthesis is an active research topic with multiple medical clinically relevant applications. Recently, methods allowing training with paired but misaligned data have started to emerge. However, no robust and well-performing methods applicable to a wide range of real world data sets exist. In this work, we propose a generic solution to the problem of cross-modality image synthesis with paired but non-aligned data by introducing new deformation equivariance encouraging loss functions. The method consists of joint training of an image synthesis network together with separate registration networks and allows adversarial training conditioned on the input even with misaligned data. The work lowers the bar for new clinical applications by allowing effortless training of cross-modality image synthesis networks for more difficult data sets.Description
Funding Information: This work was supported by Research Council of Finland (Flagship programme: Finnish Center for Artificial Intelligence [grant 345552 ] and grants 315896 , 335976 , 336033 , 341967 , 352986 , 358246 ), ERA PerMed ABCAP (Research Council of Finland grants 334774 , 334782 ), EU (H2020 grant 101016775 and NextGenerationEU). We also acknowledge the computational resources provided by the Aalto Science-IT Project. | openaire: EC/H2020/101016775/EU//INTERVENE
Keywords
Cross-modality image synthesis, Image registration, Image-to-image translation
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Citation
Honkamaa, J, Khan, U, Koivukoski, S, Valkonen, M, Latonen, L, Ruusuvuori, P & Marttinen, P 2023, ' Deformation equivariant cross-modality image synthesis with paired non-aligned training data ', Medical Image Analysis, vol. 90, 102940, pp. 1-13 . https://doi.org/10.1016/j.media.2023.102940
