CellDisc: Boosting Virtual Staining via Cell Segmentation-Guided Discrimination

Virtual staining aims to generate histochemically stained images from unstained or differently stained tissue images, yet existing methods primarily rely on image-level objectives without explicitly modeling cellular structures that are fundamental to histopathological interpretation. Moreover, rigorous evaluation remains challenging because commonly used datasets often rely on consecutive tissue sections, resulting in imperfect cell-level correspondence between source and target images. To address these limitations, we propose CellDisc, a simple yet effective cell segmentation-guided discrimination framework that introduces dense cellular supervision into adversarial learning. Instead of conventional binary discrimination, CellDisc jointly models cell localization and directional flow fields, encouraging the preservation of cellular structure and spatial organization. As a plug-and-play discriminator enhancement, CellDisc can be integrated into diverse paired GAN baselines without modifying their generators. We further establish three strongly paired same-section datasets covering H&E-to-P63, H&E-to-P63&CK, and FFPE-to-H&E transformations, enabling unified reference-based evaluation of pixel-wise, perceptual, cell-level, and stain-specific fidelity. Extensive experiments across multiple baselines and staining tasks demonstrate the broad compatibility and effectiveness of CellDisc. When integrated with P2P-res9, CellDisc achieves competitive or superior performance compared with recent GAN- and diffusion-based approaches, particularly in perceptual and cell-level fidelity. Ablation studies further demonstrate the benefit of directional flow fields and their complementarity with perceptual supervision, while evaluations using independent cell segmentation models consistently support the improvements in cellular fidelity. Finally, downstream cancer classification demonstrates that CellDisc preserves diagnostically relevant information across different classifier architectures. Overall, our results establish explicit cellular modeling as an effective and compatible strategy for improving structural fidelity in virtual staining. Code and data will be made publicly available.