Computer Science > Computer Vision and Pattern Recognition
[Submitted on 26 Aug 2022 (v1), last revised 29 Sep 2023 (this version, v2)]
Title:Deformation equivariant cross-modality image synthesis with paired non-aligned training data
View PDFAbstract: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.
Submission history
From: Joel Honkamaa [view email][v1] Fri, 26 Aug 2022 08:12:40 UTC (18,644 KB)
[v2] Fri, 29 Sep 2023 12:14:49 UTC (34,925 KB)
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