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

TL;DR

This paper introduces BANIS, a novel model that synthesizes microscopic images from multiple sources with different geometric features, enabling better cross-modality image generation for biological research.

Contribution

BANIS is the first model to incorporate geometric associations in multi-source microscopic image synthesis, advancing the realism and diversity of generated images.

Findings

Successfully synthesizes image pairs of C. elegans embryonic images.

First application to associate spatial geometric features across sources.

Demonstrates improved image quality and geometric consistency.

Abstract

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.