Segmentation-before-Staining Improves Structural Fidelity in Virtual IHC-to-Multiplex IF Translation

TL;DR

This paper presents a novel segmentation-before-staining approach that enhances the structural fidelity of virtual IHC-to-mIF translation, improving nuclear morphology accuracy without task-specific tuning.

Contribution

It introduces a supervision-free, architecture-agnostic method that incorporates a nuclei probability map as a prior, improving nuclear detail preservation in virtual staining.

Findings

Consistent improvement in nuclei count accuracy.

Enhanced perceptual quality of synthesized images.

Applicable across multiple architectures and datasets.

Abstract

Multiplex immunofluorescence (mIF) enables simultaneous single-cell quantification of multiple biomarkers within intact tissue architecture, yet its high reagent cost, multi-round staining protocols, and need for specialized imaging platforms limit routine clinical adoption. Virtual staining can synthesize mIF channels from widely available brightfield immunohistochemistry (IHC), but current translators optimize pixel-level fidelity without explicitly constraining nuclear morphology. In pathology, this gap is clinically consequential: subtle distortions in nuclei count, shape, or spatial arrangement propagate directly to quantification endpoints such as the Ki67 proliferation index, where errors of a few percent can shift treatment-relevant risk categories. This work introduces a supervision-free, architecture-agnostic conditioning strategy that injects a continuous cell probability map from a pretrained nuclei segmentation foundation model as an explicit input prior, together with a variance-preserving regularization term that matches local intensity statistics to maintain cell-level heterogeneity in synthesized fluorescence channels. The soft prior retains gradient-level boundary information lost by binary thresholding, providing a richer conditioning signal without task-specific tuning. Controlled experiments across Pix2Pix with U-Net and ResNet generators, deterministic regression U-Net, and conditional diffusion on two independent datasets demonstrate consistent improvements in nuclei count fidelity and perceptual quality, as the sole modifications. Code will be made publicly available upon acceptance.