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

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