Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Junhyeok Lee, Han Jang, Heeseong Eum, Joon Jang, Kyu Sung Choi

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.

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

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.
Paper Structure (18 sections, 5 equations, 2 figures, 2 tables)

This paper contains 18 sections, 5 equations, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Method
  3. Soft Structural Conditioning for Paired IHC-to-mIF Translation
  4. Soft Prior Generation
  5. Variance-Preserving Regularization
  6. Clinical Fidelity Metrics
  7. Ki67 positivity error.
  8. Per-pixel MAE.
  9. Experiments and Results
  10. Experimental Setup
  11. Datasets.
  12. Translation architectures.
  13. Training details.
  14. Evaluation metrics.
  15. Main Results
  16. ...and 3 more sections

Figures (2)

  • Figure 1: Overview of the proposed soft prior conditioning framework. (a) Cellpose stringer2021cellpose extracts a continuous probability map $P_\text{soft}$, concatenated as a fourth channel. (b) The conditioned input is passed to any translation architecture. (c) Multi-channel outputs; $\mathcal{L}_\text{var}$ is added to the base objective to constrain local intensity statistics.
  • Figure 2: Qualitative comparison on DeepLIIF test cases across three target channels (DAPI, Lap2, Ki67) from bladder and lung tissue.