  Microscopic images from multiple modalities can produce plentiful
experimental information. In practice, biological or physical constraints under
a given observation period may prevent researchers from acquiring enough
microscopic scanning. Recent studies demonstrate that image synthesis is one of
the popular approaches to release such constraints. Nonetheless, most existing
synthesis approaches only translate images from the source domain to the target
domain without solid geometric associations. To embrace this challenge, we
propose an innovative model architecture, BANIS, to synthesize diversified
microscopic images from multi-source domains with distinct geometric features.
The experimental outcomes indicate that BANIS successfully synthesizes
favorable image pairs on C. elegans microscopy embryonic images. To the best of
our knowledge, BANIS is the first application to synthesize microscopic images
that associate distinct spatial geometric features from multi-source domains.
