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Microscopic images from multiple modalities can produce plentifulexperimental information. In practice, biological or physical constraints undera given observation period may prevent researchers from acquiring enoughmicroscopic scanning. Recent studies demonstrate that image synthesis is one ofthe popular approaches to release such constraints. Nonetheless, most existingsynthesis approaches only translate images from the source domain to the targetdomain without solid geometric associations. To embrace this challenge, wepropose an innovative model architecture, BANIS, to synthesize diversifiedmicroscopic images from multi-source domains with distinct geometric features.The experimental outcomes indicate that BANIS successfully synthesizesfavorable image pairs on C. elegans microscopy embryonic images. To the best ofour knowledge, BANIS is the first application to synthesize microscopic imagesthat associate distinct spatial geometric features from multi-source domains.

Geometrically Matched Multi-source Microscopic Image Synthesis Using Bidirectional Adversarial Networks