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Recent developments in fluorescence microscopy allow capturinghigh-resolution 3D images over time for living model organisms. To be able toimage even large specimens, techniques like multi-view light-sheet imagingrecord different orientations at each time point that can then be fused into asingle high-quality volume. Based on measured point spread functions (PSF),deconvolution and content fusion are able to largely revert the inevitabledegradation occurring during the imaging process. Classical multi-viewdeconvolution and fusion methods mainly use iterative procedures andcontent-based averaging. Lately, Convolutional Neural Networks (CNNs) have beendeployed to approach 3D single-view deconvolution microscopy, but themulti-view case waits to be studied. We investigated the efficacy of CNN-basedmulti-view deconvolution and fusion with two synthetic data sets that mimicdeveloping embryos and involve either two or four complementary 3D views.Compared with classical state-of-the-art methods, the proposed semi- andself-supervised models achieve competitive and superior deconvolution andfusion quality in the two-view and quad-view cases, respectively.

Semi- and Self-Supervised Multi-View Fusion of 3D Microscopy Images using Generative Adversarial Networks