Geometrically Matched Multi-source Microscopic Image Synthesis Using Bidirectional Adversarial Networks
This addresses a domain-specific problem for researchers in microscopy who need more image data under biological or physical constraints, but it appears incremental as it builds on existing image synthesis approaches.
The paper tackled the problem of synthesizing microscopic images from multiple modalities with geometric associations, proposing BANIS to generate diversified images from multi-source domains with distinct geometric features, and it successfully synthesized favorable image pairs on C. elegans microscopy embryonic images.
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.