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

• URL: http://arxiv.org/abs/2603.16160v1
• Date: Tue, 17 Mar 2026 06:25:50 GMT
• Title: Segmentation-before-Staining Improves Structural Fidelity in Virtual IHC-to-Multiplex IF Translation
• Abstract summary: Multiplex immunofluorescence (mIF) enables simultaneous single-cell quantification of multiple biomarkers within intact tissue architecture.<n>Virtual staining can synthesize mIF channels from widely available brightfieldchemistry (IHC), but current translators optimize pixel-level fidelity without explicitly constraining nuclear morphology.<n>This work introduces a supervision-free, architecture-agnostic conditioning strategy that injects a continuous cell probability map from a pretrained nuclei segmentation foundation model.
• Score: 1.8848440735415501
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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