CDSB: accelerating connectomics workflow via Content-Decoupled Schrödinger Bridge

Background Large-scale connectomics requires the nanometer-scale resolution of electron microscopy to resolve ultrastructural details, but its acquisition is time- and labor-intensive. In contrast, high-throughput light microscopy offers high throughput but lacks the spatial resolution required for fine-grained analysis. To bridge this gap, we propose a computational framework for cross-modal ultrastructural inference, which maps high-throughput light microscopy data to an electron microscopy-like structural representation to accelerate downstream workflows. Rather than performing de novo synthesis, our framework recovers ultrastructural representation from diffraction-limited optical signals by enhancing latent morphological cues within the light microscopy data. This workflow is powered by a novel deep learning framework, the Content-Decoupled Schrödinger Bridge, which disentangles modality-invariant physical content from imaging-specific attributes and incorporates a physics-informed perceptual loss to ensure structural plausibility. Results Our approach accelerates the connectomics pipeline, demonstrated in three key applications. First, the enhanced clarity of the generated images reduced expert miss rates for region-of-interest selection. Second, the generated images are inherently aligned with the source light microscopy data while matching the appearance of target electron microscopy data, streamlining multi-modal registration. Third, they improve segmentation accuracy by allowing pre-trained electron microscopy models to be applied directly to light microscopy data. Conclusions In summary, this work provides a computational solution for bridging the gap between imaging speed and resolution. By enhancing the analytical value of light microscopy data, our workflow accelerates key stages of large-scale connectomics mapping, from targeted acquisition to quantitative analysis.